diff --git a/main.bib b/main.bib index 082e015..f98a209 100644 --- a/main.bib +++ b/main.bib @@ -1,1223 +1,989 @@ -@misc{taylor_forecasting_2017, - title = {Forecasting at scale}, - copyright = {http://creativecommons.org/licenses/by/4.0/}, - url = {https://peerj.com/preprints/3190v2}, - doi = {10.7287/peerj.preprints.3190v2}, - abstract = {Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and high quality forecasts –especially when there are a variety of time series and analysts with expertise in time series modeling are relatively rare. To address these challenges, we describe a practical approach to forecasting “at scale” that combines configurable models with analyst-in-the-loop performance analysis. We propose a modular regression model with interpretable parameters that can be intuitively adjusted by analysts with domain knowledge about the time series. We describe performance analyses to compare and evaluate forecasting procedures, and automatically flag forecasts for manual review and adjustment. Tools that help analysts to use their expertise most effectively enable reliable, practical forecasting of business time series.}, - language = {en}, - urldate = {2024-10-14}, - publisher = {PeerJ Preprints}, - author = {Taylor, Sean J and Letham, Benjamin}, - month = sep, - year = {2017}, - file = {PDF:/home/alex/Zotero/storage/GK5AIG2V/Taylor and Letham - 2017 - Forecasting at scale.pdf:application/pdf}, +@article{gaskins_predictors_2018, + title = {Predictors of sexual intercourse frequency among couples trying to conceive}, + volume = {15}, + issn = {1743-6095}, + url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882561/}, + doi = {10.1016/j.jsxm.2018.02.005}, + abstract = {Background +Little is known about the predictors of sexual intercourse frequency ({SIF}) among couples trying to conceive despite the well-established link between {SIF} and fecundity. + +Aim +To evaluate the male and female demographic, occupational, and lifestyle predictors of {SIF} among couples. + +Methods +469 couples without a history of infertility participating in the Longitudinal Investigation of Fertility and the Environment Study (2005–2009) were followed for ≤1 year while trying to conceive. At enrollment, both partners were interviewed about demographic, occupational, lifestyle, and psychological characteristics using standardized questionnaires. Multivariable generalized linear mixed models with Poisson distribution was used to estimate the adjusted percent difference in {SIF} across exposure categories. + +Outcomes +{SIF} was recorded in daily journals and summarized as average {SIF} per month. + +Results +The median (interquartile range) {SIF} during follow-up was 6 (4–9) acts per month. For every year increase in female and male age, {SIF} decreased by −0.8\% (95\% {CI} −2.5, 1.0\%) and −1.7\% (95\% {CI} −3.1, −0.3\%). Women with high school education or less and those of non-White race had 34.4\% and 16.0\% higher {SIF}, respectively. A similar trend was seen for male education and race. Only couples where both partners (but not just one partner) worked rotating shifts had −39.1\% (95\% {CI} −61.0, −5.0\%) lower {SIF} compared to couples where neither partner worked rotating shifts. Male (but not female) exercise was associated with 13.2\% (95\% {CI} 1.7, 26.0\%) higher {SIF}. Diagnosis of a mood or anxiety disorder in the male (but not female) was associated with a 26.0\% (95\% {CI} −42.7, −4.4\%) lower {SIF}. Household income, smoking status, {BMI}, night work, alcohol intake, psychosocial stress were not associated with {SIF}. + +Clinical Implications +Even among couples trying to conceive, there was substantial variation in {SIF}. Both partners’ age, education, race, and rotating shift work as well as male exercise and mental health play an important role in determining {SIF}. + +Strengths \& Limitations +As this was a secondary analysis of an existing study, we lacked information on many pertinent psychological and relationship quality variables and the hormonal status of participants, which could have affected {SIF}. The unique population-based couple design, however, captured both partners’ demographics, occupational characteristics, lifestyle behaviors in advance of their daily, prospective reporting of {SIF}, which was a major strength. + +Conclusion +Important predictors of {SIF} among couples attempting to conceive include male exercise and mental health and both partners’ age, education, race, and rotating shift work.}, + pages = {519--528}, + number = {4}, + journaltitle = {J Sex Med}, + author = {Gaskins, Audrey J. and Sundaram, Rajeshwari and Buck Louis, Germaine M. and Chavarro, Jorge E.}, + urldate = {2025-07-30}, + date = {2018-04}, + pmid = {29523477}, + pmcid = {PMC5882561}, + file = {Full Text PDF:/home/alex/Zotero/storage/S3JV4TU2/Gaskins et al. - 2018 - Predictors of sexual intercourse frequency among couples trying to conceive.pdf:application/pdf}, } -@book{hutter_machine_2021, - address = {Cham}, - series = {Lecture {Notes} in {Computer} {Science}}, - title = {Machine {Learning} and {Knowledge} {Discovery} in {Databases}: {European} {Conference}, {ECML} {PKDD} 2020, {Ghent}, {Belgium}, {September} 14–18, 2020, {Proceedings}, {Part} {III}}, - volume = {12459}, - copyright = {https://www.springernature.com/gp/researchers/text-and-data-mining}, - isbn = {978-3-030-67663-6 978-3-030-67664-3}, - shorttitle = {Machine {Learning} and {Knowledge} {Discovery} in {Databases}}, - url = {https://link.springer.com/10.1007/978-3-030-67664-3}, - language = {en}, - urldate = {2024-10-14}, - publisher = {Springer International Publishing}, - editor = {Hutter, Frank and Kersting, Kristian and Lijffijt, Jefrey and Valera, Isabel}, - year = {2021}, - doi = {10.1007/978-3-030-67664-3}, - file = {Submitted Version:/home/alex/Zotero/storage/JMVJMLJ5/Hutter et al. - 2021 - Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2020, Ghent, B.pdf:application/pdf}, +@article{pearl_factors_1933, + title = {{FACTORS} {IN} {HUMAN} {FERTILITY} {AND} {THEIR} {STATISTICAL} {EVALUATION}}, + volume = {222}, + rights = {https://www.elsevier.com/tdm/userlicense/1.0/}, + issn = {01406736}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0140673601186484}, + doi = {10.1016/S0140-6736(01)18648-4}, + pages = {607--611}, + number = {5741}, + journaltitle = {The Lancet}, + author = {Pearl, Raymond}, + urldate = {2025-07-30}, + date = {1933-09}, + langid = {english}, } -@incollection{hutter_general_2021, - address = {Cham}, - title = {A {General} {Machine} {Learning} {Framework} for {Survival} {Analysis}}, - volume = {12459}, - isbn = {978-3-030-67663-6 978-3-030-67664-3}, - url = {https://link.springer.com/10.1007/978-3-030-67664-3_10}, - language = {en}, - urldate = {2024-10-14}, - booktitle = {Machine {Learning} and {Knowledge} {Discovery} in {Databases}}, - publisher = {Springer International Publishing}, - author = {Bender, Andreas and Rügamer, David and Scheipl, Fabian and Bischl, Bernd}, - editor = {Hutter, Frank and Kersting, Kristian and Lijffijt, Jefrey and Valera, Isabel}, - year = {2021}, - doi = {10.1007/978-3-030-67664-3_10}, - note = {Series Title: Lecture Notes in Computer Science}, - pages = {158--173}, - file = {Submitted Version:/home/alex/Zotero/storage/WQIHZ7IP/Bender et al. - 2021 - A General Machine Learning Framework for Survival Analysis.pdf:application/pdf}, +@misc{goyal_accurate_2018, + title = {Accurate, Large Minibatch {SGD}: Training {ImageNet} in 1 Hour}, + url = {http://arxiv.org/abs/1706.02677}, + doi = {10.48550/arXiv.1706.02677}, + shorttitle = {Accurate, Large Minibatch {SGD}}, + abstract = {Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous {SGD} offers a potential solution to this problem by dividing {SGD} minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the {SGD} minibatch size. In this paper, we empirically show that on the {ImageNet} dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains {ResNet}-50 with a minibatch size of 8192 on 256 {GPUs} in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves {\textasciitilde}90\% scaling efficiency when moving from 8 to 256 {GPUs}. Our findings enable training visual recognition models on internet-scale data with high efficiency.}, + number = {{arXiv}:1706.02677}, + publisher = {{arXiv}}, + author = {Goyal, Priya and Dollár, Piotr and Girshick, Ross and Noordhuis, Pieter and Wesolowski, Lukasz and Kyrola, Aapo and Tulloch, Andrew and Jia, Yangqing and He, Kaiming}, + urldate = {2025-07-22}, + date = {2018-04-30}, + eprinttype = {arxiv}, + eprint = {1706.02677 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Distributed, Parallel, and Cluster Computing}, + file = {Full Text PDF:/home/alex/Zotero/storage/5MKPLWI7/Goyal et al. - 2018 - Accurate, Large Minibatch SGD Training ImageNet in 1 Hour.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/TRYJFLDW/1706.html:text/html}, } -@misc{lightningai_pytorch_2024, - title = {{PyTorch} {Lightning}}, - url = {https://www.pytorchlightning.ai}, - urldate = {2024-10-14}, - author = {lightning.ai}, - year = {2024}, +@article{wen_time_2023, + title = {Time Series Prediction Based on {LSTM}-Attention-{LSTM} Model}, + volume = {11}, + issn = {2169-3536}, + url = {https://ieeexplore.ieee.org/document/10124729/}, + doi = {10.1109/ACCESS.2023.3276628}, + abstract = {Time series forecasting uses data from the past periods of time to predict future information, which is of great significance in many applications. Existing time series forecasting methods still have problems such as low accuracy when dealing with some non-stationary multivariate time series data forecasting. Aiming at the shortcomings of existing methods, in this paper we propose a new time series forecasting model {LSTM}-attention-{LSTM}. The model uses two {LSTM} models as the encoder and decoder, and introduces an attention mechanism between the encoder and decoder. The model has two distinctive features: first, by using the attention mechanism to calculate the interrelationship between sequence data, it overcomes the disadvantage of the coder-and-decoder model in that the decoder cannot obtain sufficiently long input sequences; second, it is suitable for sequence forecasting with long time steps. In this paper we validate the proposed model based on several real data sets, and the results show that the {LSTM}-attention-{LSTM} model is more accurate than some currently dominant models in prediction. The experiment also assessed the effect of the attention mechanism at different time steps by varying the time step.}, + pages = {48322--48331}, + journaltitle = {{IEEE} Access}, + author = {Wen, Xianyun and Li, Weibang}, + urldate = {2025-07-22}, + date = {2023}, + keywords = {attention mechanisms, Autoregressive processes, Data models, Decoding, encoder and decoder model, Forecasting, Logic gates, long short-term memory networks, Predictive models, Time series analysis, Time series forecasting}, + file = {Full Text PDF:/home/alex/Zotero/storage/3M54PVSE/Wen and Li - 2023 - Time Series Prediction Based on LSTM-Attention-LSTM Model.pdf:application/pdf}, } -@misc{alexandrov_gluonts_2019, - title = {{GluonTS}: {Probabilistic} {Time} {Series} {Models} in {Python}}, - shorttitle = {{GluonTS}}, - url = {http://arxiv.org/abs/1906.05264}, - abstract = {We introduce Gluon Time Series (GluonTS, available at https://gluon-ts.mxnet.io), a library for deep-learning-based time series modeling. GluonTS simplifies the development of and experimentation with time series models for common tasks such as forecasting or anomaly detection. It provides all necessary components and tools that scientists need for quickly building new models, for efficiently running and analyzing experiments and for evaluating model accuracy.}, - urldate = {2024-10-14}, - publisher = {arXiv}, - author = {Alexandrov, Alexander and Benidis, Konstantinos and Bohlke-Schneider, Michael and Flunkert, Valentin and Gasthaus, Jan and Januschowski, Tim and Maddix, Danielle C. and Rangapuram, Syama and Salinas, David and Schulz, Jasper and Stella, Lorenzo and Türkmen, Ali Caner and Wang, Yuyang}, - month = jun, - year = {2019}, - note = {arXiv:1906.05264}, - keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, - file = {Preprint PDF:/home/alex/Zotero/storage/JP9K74A8/Alexandrov et al. - 2019 - GluonTS Probabilistic Time Series Models in Python.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/RJYSBT29/1906.html:text/html}, +@misc{pham_dropout_2014, + title = {Dropout improves Recurrent Neural Networks for Handwriting Recognition}, + url = {http://arxiv.org/abs/1312.4569}, + doi = {10.48550/arXiv.1312.4569}, + abstract = {Recurrent neural networks ({RNNs}) with Long Short-Term memory cells currently hold the best known results in unconstrained handwriting recognition. We show that their performance can be greatly improved using dropout - a recently proposed regularization method for deep architectures. While previous works showed that dropout gave superior performance in the context of convolutional networks, it had never been applied to {RNNs}. In our approach, dropout is carefully used in the network so that it does not affect the recurrent connections, hence the power of {RNNs} in modeling sequence is preserved. Extensive experiments on a broad range of handwritten databases confirm the effectiveness of dropout on deep architectures even when the network mainly consists of recurrent and shared connections.}, + number = {{arXiv}:1312.4569}, + publisher = {{arXiv}}, + author = {Pham, Vu and Bluche, Théodore and Kermorvant, Christopher and Louradour, Jérôme}, + urldate = {2025-07-22}, + date = {2014-03-10}, + eprinttype = {arxiv}, + eprint = {1312.4569 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing}, + file = {Full Text PDF:/home/alex/Zotero/storage/IA52LNE8/Pham et al. - 2014 - Dropout improves Recurrent Neural Networks for Handwriting Recognition.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/LJ8EJ5PS/1312.html:text/html}, } -@misc{cho_learning_2014, - title = {Learning {Phrase} {Representations} using {RNN} {Encoder}-{Decoder} for {Statistical} {Machine} {Translation}}, - url = {http://arxiv.org/abs/1406.1078}, - abstract = {In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.}, - urldate = {2024-10-10}, - publisher = {arXiv}, - author = {Cho, Kyunghyun and Merrienboer, Bart van and Gulcehre, Caglar and Bahdanau, Dzmitry and Bougares, Fethi and Schwenk, Holger and Bengio, Yoshua}, - month = sep, - year = {2014}, - note = {arXiv:1406.1078}, - keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Neural and Evolutionary Computing}, - file = {Preprint PDF:/home/alex/Zotero/storage/E8WMK2IN/Cho et al. - 2014 - Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/6PTCL8LW/1406.html:text/html}, +@article{wallach_prediction_1980, + title = {Prediction and Detection of Ovulation}, + volume = {34}, + issn = {00150282}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0015028216448880}, + doi = {10.1016/S0015-0282(16)44888-0}, + pages = {89--98}, + number = {2}, + journaltitle = {Fertility and Sterility}, + author = {Wallach, Edward and Moghissi, Kamran S.}, + urldate = {2025-07-15}, + date = {1980-08}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/JREACGRA/Wallach and Moghissi - 1980 - Prediction and Detection of Ovulation.pdf:application/pdf}, +} + +@article{guida_efficacy_1999, + title = {Efficacy of methods for determining ovulation in a natural family planning program}, + volume = {72}, + issn = {00150282}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0015028299003659}, + doi = {10.1016/S0015-0282(99)00365-9}, + abstract = {Objective: To evaluate the efficacy in ovulation detection of methods used in natural family planning in comparison with pelvic ultrasonography. Design: Prospective analysis of ovulation detection by natural family planning methods and ultrasonography. Setting: Natural family planning clinic, Department of Obstetrics and Gynecology, University of Naples “Federico {II}”. Patient(s): Forty healthy women who were highly motivated to use natural family planning. Intervention(s): None. Main Outcome Measure(s): Transvaginal ultrasonographic findings, urinary {LH} levels, salivary b-glucuronidase activity, salivary ferning levels and characteristics of cervical mucus, and {BBT}. +Result(s): Urinary {LH} level determination yielded a 100\% correlation with the simultaneous ultrasonographic diagnosis of ovulation. Mucus sensations and characteristics yielded a 48.3\% correlation when simultaneously evaluated with ovulation. b-Glucuronidase levels yielded a 27.7\% correlation. The salivary ferning test had a 36.8\% ovulation-detection rate the day of ovulation, but 58.7\% of results were uninterpretable. Body temperature measurements yielded a 30.4\% correlation with the simultaneous ultrasonographic diagnosis of ovulation. +Conclusion(s): Measuring urinary {LH} levels is an excellent method for determining ovulation. Although variations in mucus characteristics and basal body temperature correlate somewhat with ovulation, the length of the fertile period is overestimated with these methods. The salivary ferning test and measurement of b-glucuronidase levels are not good methods for home ovulation testing. (Fertil Sterilt 1999;72:900 – 4. ©1999 by American Society for Reproductive Medicine.)}, + pages = {900--904}, + number = {5}, + journaltitle = {Fertility and Sterility}, + author = {Guida, Maurizio and Tommaselli, Giovanni A and Palomba, Stefano and Pellicano, Massimiliano and Moccia, Gianfranco and Di Carlo, Costantino and Nappi, Carmine}, + urldate = {2025-07-15}, + date = {1999-11}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/UAS9AGTU/Guida et al. - 1999 - Efficacy of methods for determining ovulation in a natural family planning program.pdf:application/pdf}, +} + +@article{thigpen_oura_2025, + title = {Oura Ring as a Tool for Ovulation Detection: Validation Analysis}, + volume = {27}, + issn = {1438-8871}, + url = {https://www.jmir.org/2025/1/e60667}, + doi = {10.2196/60667}, + shorttitle = {Oura Ring as a Tool for Ovulation Detection}, + abstract = {Background: Oura Ring is a wearable device that estimates ovulation dates using physiology data recorded from the finger. Estimating the ovulation date can aid fertility management for conception or nonhormonal contraception and provides insights into follicular and luteal phase lengths. Across the reproductive lifespan, changes in these phase lengths can serve as a biomarker for reproductive health. +Objective: We assessed the strengths, weaknesses, and limitations of using physiology from the Oura Ring to estimate the ovulation date. We compared performance across cycle length, cycle variability, and participant age. In each subgroup, we compared the algorithm’s performance with the traditional calendar method, which estimates the ovulation date based on an individual’s last period start date and average menstrual cycle length. +Methods: The study sample contained 1155 ovulatory menstrual cycles from 964 participants recruited from the Oura Ring commercial database. Ovulation prediction kits served as a benchmark to evaluate the performance. The Fisher test was used to determine an odds ratio to assess if ovulation detection rate significantly differed between methods or subgroups. The Mann-Whitney U test was used to determine if the accuracy of the estimated ovulation date differed between the estimated and reference ovulation dates. +Results: The physiology method detected 1113 (96.4\%) of 1155 ovulations with an average error of 1.26 days, which was significantly lower (U=904942.0, P{\textless}.001) than the calendar method’s average error of 3.44 days. The physiology method had significantly better accuracy across all cycle lengths, cycle variability groups, and age groups compared with the calendar method (P{\textless}.001). The physiology method detected fewer ovulations in short cycles (odds ratio 3.56, 95\% {CI} 1.65-8.06; P=.008) but did not differ between typical and long or abnormally long cycles. Abnormally long cycle lengths were associated with decreased accuracy (U=22,383, P=.03), with a mean absolute error of 1.7 ({SEM} .09) days compared with 1.18 ({SEM} .02) days. The physiology method was not associated with differences in accuracy across age or typical cycle variability, while the calendar method performed significantly worse in participants with irregular cycles (U=21,643, P{\textless}.001). +Conclusions: The physiology method demonstrated superior accuracy over the calendar method, with approximately 3-fold improvement. Calendar-based fertility tracking could be used as a backup in cases of insufficient physiology data but should be used with caution, particularly for individuals with irregular menstrual cycles. Our analyses suggest the physiology method can reliably estimate ovulation dates for adults aged 18-52 years, across a variety of cycle lengths, and in users with regular or irregular cycles. This method may be used as a tool to improve fertile window estimation, which can aid in conceiving or preventing pregnancies. This method also offers a low-effort solution for follicular and luteal phase length tracking, which are key biomarkers for reproductive health.}, + pages = {e60667}, + journaltitle = {J Med Internet Res}, + author = {Thigpen, Nina and Patel, Shyamal and Zhang, Xi}, + urldate = {2025-07-15}, + date = {2025-01-31}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/TECKQLN7/Thigpen et al. - 2025 - Oura Ring as a Tool for Ovulation Detection Validation Analysis.pdf:application/pdf}, +} + +@online{noauthor_pytorch_nodate, + title = {{PyTorch}}, + url = {https://pytorch.org/}, + abstract = {{PyTorch} Foundation is the deep learning community home for the open source {PyTorch} framework and ecosystem.}, + titleaddon = {{PyTorch}}, + urldate = {2025-07-11}, + langid = {american}, + file = {Snapshot:/home/alex/Zotero/storage/K7CTLE77/pytorch.org.html:text/html}, +} + +@article{li_menstrual_2023, + title = {Menstrual cycle length variation by demographic characteristics from the Apple Women’s Health Study}, + volume = {6}, + issn = {2398-6352}, + url = {https://www.nature.com/articles/s41746-023-00848-1}, + doi = {10.1038/s41746-023-00848-1}, + abstract = {Abstract + + Menstrual characteristics are important signs of women’s health. Here we examine the variation of menstrual cycle length by age, ethnicity, and body weight using 165,668 cycles from 12,608 participants in the {US} using mobile menstrual tracking apps. After adjusting for all covariates, mean menstrual cycle length is shorter with older age across all age groups until age 50 and then became longer for those age 50 and older. Menstrual cycles are on average 1.6 (95\%{CI}: 1.2, 2.0) days longer for Asian and 0.7 (95\%{CI}: 0.4, 1.0) days longer for Hispanic participants compared to white non-Hispanic participants. Participants with {BMI} ≥ 40 kg/m + 2 + have 1.5 (95\%{CI}: 1.2, 1.8) days longer cycles compared to those with {BMI} between 18.5 and 25 kg/m + 2 + . Cycle variability is the lowest among participants aged 35–39 but are considerably higher by 46\% (95\%{CI}: 43\%, 48\%) and 45\% (95\%{CI}: 41\%, 49\%) among those aged under 20 and between 45–49. Cycle variability increase by 200\% (95\%{CI}: 191\%, 210\%) among those aged above 50 compared to those in the 35–39 age group. Compared to white participants, those who are Asian and Hispanic have larger cycle variability. Participants with obesity also have higher cycle variability. Here we confirm previous observations of changes in menstrual cycle pattern with age across reproductive life span and report new evidence on the differences of menstrual variation by ethnicity and obesity status. Future studies should explore the underlying determinants of the variation in menstrual characteristics.}, + pages = {100}, + number = {1}, + journaltitle = {npj Digit. Med.}, + author = {Li, Huichu and Gibson, Elizabeth A. and Jukic, Anne Marie Z. and Baird, Donna D. and Wilcox, Allen J. and Curry, Christine L. and Fischer-Colbrie, Tyler and Onnela, Jukka-Pekka and Williams, Michelle A. and Hauser, Russ and Coull, Brent A. and Mahalingaiah, Shruthi}, + urldate = {2025-07-04}, + date = {2023-05-29}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/9J5N6YIW/Li et al. - 2023 - Menstrual cycle length variation by demographic characteristics from the Apple Women’s Health Study.pdf:application/pdf}, +} + +@article{ecochard_menstrual_2024, + title = {The menstrual cycle is influenced by weekly and lunar rhythms}, + volume = {121}, + issn = {00150282}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0015028223020769}, + doi = {10.1016/j.fertnstert.2023.12.009}, + abstract = {Objective: To study whether the menstrual cycle has a circaseptan (7 days) rhythm and whether it is associated with the lunar cycle (also defined as the synodic month, it is the cycle of the phases of the Moon as seen from Earth, averaging 29.5 days in length). Design: Cross-sectional study. Subjects: A total of 35,940 European and North American women aged 18–40 years. Exposure: Data were collected in real-life conditions. Intervention: No intervention was performed. Main Outcome Measure: The onset of menstruation was assessed in prospectively measured menstrual cycles (311,064 cycles) over 3 full years (2019–2021). Associations were calculated between the onset of menstruation and the day of the week, and between the onset of menstruation and the lunar phase. +Results: In this large data set, a circaseptan (7-day) rhythmicity of menstruation was observed, with a peak (acrophase) of menstrual onset on Thursdays and Fridays. This circaseptan rhythm was observed in every age group, in every phase of the lunar cycle, and in all seasons. This feature was most pronounced for cycle durations between 27 and 29 days. In winter, the circaseptan rhythm was found in cycles of 27–29 days, but not in other cycle lengths. A circalunar rhythm was also statistically significant, but not as clearly defined as the circaseptan rhythm. The peak (acrophase) of the circalunar rhythm of menstrual onset varied according to the season. In addition, there was a small but statistically significant interaction between the circaseptan rhythm and the lunar cycle. +Conclusion: Although relatively small in amplitude, the weekly rhythm of menstruation was statistically significant. Menstruation occurs more often on Thursdays and Fridays than on other days of the week. This is particularly true for women whose cycles last between 27 and 29 days. Circalunar rhythmicity was also statistically significant. However, it is less pronounced than the weekly rhythm. (Fertil {SterilÒ} 2024;121:651-9. Ó2023 by American Society for Reproductive Medicine.)}, + pages = {651--659}, + number = {4}, + journaltitle = {Fertility and Sterility}, + author = {Ecochard, René and Leiva, Rene and Bouchard, Thomas P. and Van Lamsweerde, Agathe and Pearson, Jack T. and Stanford, Joseph B. and Gronfier, Claude}, + urldate = {2025-07-03}, + date = {2024-04}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/T6DNB4M9/Ecochard et al. - 2024 - The menstrual cycle is influenced by weekly and lunar rhythms.pdf:application/pdf}, +} + +@online{noauthor_kegg_nodate, + title = {kegg® fertility monitor \& kegel ball {\textbar} Track Key Fertility Metric}, + url = {https://kegg.tech/}, + abstract = {Plan your pregnancy with confidence ... kegg® is a medical-grade fertility device that gives you accurate and personalized fertility tracking through cervical mucus.}, + titleaddon = {kegg}, + urldate = {2025-07-02}, + langid = {english}, + file = {Snapshot:/home/alex/Zotero/storage/HNFFJPSN/kegg.tech.html:text/html}, +} + +@article{moreno_temporal_1988, + title = {{TEMPORAL} {RELATION} {OF} {OVDLATION} {TO} {SALIVARY} {AND} {VAGINAL} {ELECTRICAL} {RESISTANCE} {PATTERNS}: {IMPLICATIONS} {FOR} {NATURAL} {FAMILY} {PLANNING}}, + abstract = {An independent assessment of the {CUETM} Monitor (Zetek, Aurora, Colorado) as an ovulation predictor was made with emphasis on its potential role in "natural family planning". The device provides a digital measurement of the electrical resistance of saliva and vaginal secretions. Twenty-nine menstrual cycles from 11 regularly cycling women were monitored with basal temperatures, urinary {LH}, pelvic ultrasound and the {CUE} monitor. Patterns of peak salivary electrical resistance were able to predict ovulation on average 5.3 (51.9 {SD}) days in advance. Despite variations in total length of the follicular phase from cycle to cycle, the within-subject variation of this predictive interval was quite small. Nadirs in the electrical resistance of vaginal secretions occurred within 2 days of ovulation in all but one patient. Variation in this interval from cycle-tocycle was small as well. We propose an algorithm for the use of these intervals in "natural family planning" that could safely reduce the monthly abstinence period of present methods. The simplicity, objectivity and consistency of this device could result in their greater general acceptance.}, + author = {Moreno, Jorge E and Doody, Michael C and Besch, Paige}, + date = {1988-10}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/FFW8KBZQ/Moreno et al. - TEMPORAL RELATION OF OVDLATION TO SALIVARY AND VAGINAL ELECTRICAL RESISTANCE PATTERNS IMPLICATIONS.pdf:application/pdf}, +} + +@online{noauthor_trackle_nodate, + title = {trackle - einfach hormonfrei verhüten}, + url = {https://trackle.de/}, + abstract = {Das trackle Sensorsystem hilft Dir, einfach, sicher und hormonfrei zu verhüten. Jetzt informieren und symptothermale Methode nutzen!}, + titleaddon = {trackle}, + urldate = {2025-07-02}, + langid = {german}, + file = {Snapshot:/home/alex/Zotero/storage/KNFY8X2K/trackle.de.html:text/html}, +} + +@article{zhu_accuracy_2021, + title = {The Accuracy of Wrist Skin Temperature in Detecting Ovulation Compared to Basal Body Temperature: Prospective Comparative Diagnostic Accuracy Study}, + volume = {23}, + issn = {1438-8871}, + url = {https://www.jmir.org/2021/6/e20710}, + doi = {10.2196/20710}, + shorttitle = {The Accuracy of Wrist Skin Temperature in Detecting Ovulation Compared to Basal Body Temperature}, + abstract = {Background: As a daily point measurement, basal body temperature ({BBT}) might not be able to capture the temperature shift in the menstrual cycle because a single temperature measurement is present on the sliding scale of the circadian rhythm. Wrist skin temperature measured continuously during sleep has the potential to overcome this limitation. +Objective: This study compares the diagnostic accuracy of these two temperatures for detecting ovulation and to investigate the correlation and agreement between these two temperatures in describing thermal changes in menstrual cycles. +Methods: This prospective study included 193 cycles (170 ovulatory and 23 anovulatory) collected from 57 healthy women. Participants wore a wearable device (Ava Fertility Tracker bracelet 2.0) that continuously measured the wrist skin temperature during sleep. Daily {BBT} was measured orally and immediately upon waking up using a computerized fertility tracker with a digital thermometer (Lady-Comp). An at-home luteinizing hormone test was used as the reference standard for ovulation. The diagnostic accuracy of using at least one temperature shift detected by the two temperatures in detecting ovulation was evaluated. For ovulatory cycles, repeated measures correlation was used to examine the correlation between the two temperatures, and mixed effect models were used to determine the agreement between the two temperature curves at different menstrual phases. +Results: Wrist skin temperature was more sensitive than {BBT} (sensitivity 0.62 vs 0.23; P{\textless}.001) and had a higher true-positive rate (54.9\% vs 20.2\%) for detecting ovulation; however, it also had a higher false-positive rate (8.8\% vs 3.6\%), resulting in lower specificity (0.26 vs 0.70; P=.002). The probability that ovulation occurred when at least one temperature shift was detected was 86.2\% for wrist skin temperature and 84.8\% for {BBT}. Both temperatures had low negative predictive values (8.8\% for wrist skin temperature and 10.9\% for {BBT}). Significant positive correlation between the two temperatures was only found in the follicular phase (rmcorr correlation coefficient=0.294; P=.001). Both temperatures increased during the postovulatory phase with a greater increase in the wrist skin temperature (range of increase: 0.50 °C vs 0.20 °C). During the menstrual phase, the wrist skin temperature exhibited a greater and more rapid decrease (from 36.13 °C to 35.80 °C) than {BBT} (from 36.31 °C to 36.27 °C). During the preovulatory phase, there were minimal changes in both temperatures and small variations in the estimated daily difference between the two temperatures, indicating an agreement between the two curves. +Conclusions: For women interested in maximizing the chances of pregnancy, wrist skin temperature continuously measured during sleep is more sensitive than {BBT} for detecting ovulation. The difference in the diagnostic accuracy of these methods was likely attributed to the greater temperature increase in the postovulatory phase and greater temperature decrease during the menstrual phase for the wrist skin temperatures.}, + pages = {e20710}, + number = {6}, + journaltitle = {J Med Internet Res}, + author = {Zhu, Tracy Y and Rothenbühler, Martina and Hamvas, Györgyi and Hofmann, Anja and Welter, {JoEllen} and Kahr, Maike and Kimmich, Nina and Shilaih, Mohaned and Leeners, Brigitte}, + urldate = {2025-07-02}, + date = {2021-06-08}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/PY7HCR3K/Zhu et al. - 2021 - The Accuracy of Wrist Skin Temperature in Detecting Ovulation Compared to Basal Body Temperature Pr.pdf:application/pdf}, +} + +@article{shilaih_modern_2018, + title = {Modern fertility awareness methods: wrist wearables capture the changes in temperature associated with the menstrual cycle}, + volume = {38}, + issn = {0144-8463}, + url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6265623/}, + doi = {10.1042/BSR20171279}, + shorttitle = {Modern fertility awareness methods}, + abstract = {Core and peripheral body temperatures are affected by changes in reproductive hormones during the menstrual cycle. Women worldwide use the basal body temperature ({BBT}) method to aid and prevent conception. However, prior research suggests that taking one’s daily temperature can prove inconvenient and subject to environmental factors. We investigate whether a more automatic, non-invasive temperature measurement system can detect changes in temperature across the menstrual cycle. We examined how wrist skin temperature ({WST}), measured with wearable sensors, correlates with urinary tests of ovulation and may serve as a new method of fertility tracking. One hundred and thirty-six eumenorrheic, non-pregnant women participated in an observational study. Participants wore {WST} biosensors during sleep and reported their daily activities. An at-home luteinizing hormone ({LH}) test was used to confirm ovulation. {WST} was recorded across 437 cycles (mean cycles/participant = 3.21, S.D. = 2.25). We tested the relationship between the fertile window and {WST} temperature shifts, using the {BBT} three-over-six rule. A sustained 3-day temperature shift was observed in 357/437 cycles (82\%), with the lowest cycle temperature occurring in the fertile window 41\% of the time. Most temporal shifts (307/357, 86\%) occurred on ovulation day ({OV}) or later. The average early-luteal phase temperature was 0.33°C higher than in the fertile window. Menstrual cycle changes in {WST} were impervious to lifestyle factors, like having sex, alcohol, or eating prior to bed, that, in prior work, have been shown to obfuscate {BBT} readings. Although currently costlier than {BBT}, the present study suggests that {WST} could be a promising, convenient parameter for future multiparameter fertility awareness methods.}, + pages = {BSR20171279}, + number = {6}, + journaltitle = {Biosci Rep}, + author = {Shilaih, Mohaned and Goodale, Brianna M. and Falco, Lisa and Kübler, Florian and De Clerck, Valerie and Leeners, Brigitte}, + urldate = {2025-07-02}, + date = {2018-11-30}, + pmid = {29175999}, + pmcid = {PMC6265623}, + file = {Full Text PDF:/home/alex/Zotero/storage/CXNSXAFC/Shilaih et al. - 2018 - Modern fertility awareness methods wrist wearables capture the changes in temperature associated wi.pdf:application/pdf}, +} + +@online{sl_ava_nodate, + title = {Ava Fertility Tracker}, + url = {https://www.avawomen.com/}, + abstract = {See your 5 best days to conceive in real-time. Go beyond ovulation day, and make use of your full fertile window to increase your chances of pregnancy}, + titleaddon = {{AvaWomen}}, + author = {S.L, Ava Women}, + urldate = {2025-07-02}, + langid = {english}, + file = {Snapshot:/home/alex/Zotero/storage/SMIE7YIR/www.avawomen.com.html:text/html}, +} + +@online{noauthor_fact_sheet_studie_210621_2025, + title = {fact\_sheet\_studie\_210621}, + url = {https://dfxyyqidohkoi.cloudfront.net/media/filer_public/ff/86/ff8646d2-8d33-445c-9148-5979b60abbae/fact_sheet_studie_210621.pdf}, + shorttitle = {daysy\_fact\_sheet}, + urldate = {2025-07-02}, + date = {2025-07-02}, + file = {PDF:/home/alex/Zotero/storage/8VDBFS2G/fact_sheet_studie_210621.pdf:application/pdf}, +} + +@online{electronics_zykluscomputer_nodate, + title = {Zykluscomputer Daysy - 100 \% natürlich und sehr genau!}, + url = {https://de.daysy.me/}, + abstract = {Daysy Zykluscomputer - einfach, hormonfrei \& über 99\% genau. ✓ Medizinprodukt zur Berechnung Deiner fruchtbaren Tage ✓ Natürliche Familienplanung ✓ Erhöhe Deine Lebensqualität!}, + author = {Electronics, Valley}, + urldate = {2025-07-02}, + langid = {german}, + file = {Snapshot:/home/alex/Zotero/storage/ZGIZ669S/de.daysy.me.html:text/html}, +} + +@online{noauthor_natural_nodate, + title = {Natural Cycles: Natural Birth Control {\textbar} No Hormones or Side Effects}, + url = {https://www.naturalcycles.com}, + shorttitle = {Natural Cycles}, + abstract = {Natural Cycles birth control is 93\% effective with typical use and 98\% effective with perfect use. Learn more about hormone-free birth control today.}, + titleaddon = {Natural Cycles}, + urldate = {2025-07-02}, + langid = {american}, + file = {Snapshot:/home/alex/Zotero/storage/MCUDFIHC/www.naturalcycles.com.html:text/html}, +} + +@article{weiss_confirmation_2022, + title = {Confirmation of human ovulation in assisted reproduction using an adhesive axillary thermometer ({femSense}®)}, + volume = {4}, + issn = {2673-253X}, + url = {https://www.frontiersin.org/articles/10.3389/fdgth.2022.930010/full}, + doi = {10.3389/fdgth.2022.930010}, + abstract = {Objective + Timing for sexual intercourse is important in achieving pregnancy in natural menstrual cycles. Different methods of detecting the fertile window have been invented, among them luteinization hormone ({LH}) to predict ovulation and biphasic body basal temperature ({BBT}) to confirm ovulation retrospectively. The gold standard to detect ovulation in gynecology practice remains transvaginal ultrasonography in combination with serum progesterone. In this study we evaluated a wearable temperature sensing patch ({femSense}®) using continuous body temperature measurement to confirm ovulation and determine the end of the fertile window. + + + Methods + 96 participants received the {femSense}® system consisting of an adhesive axillary thermometer patch and a smartphone application, where patients were asked to document information about their previous 3 cycles. Based on the participants data, the app predicted the cycle length and the estimated day of ovulation. From these predictions, the most probable fertile window and the day for applying the patch were derived. Participants applied and activated the {femSense}® patch on the calculated date, from which the patch continuously recorded their body temperature throughout a period of up to 7 days to confirm ovulation. Patients documented their daily urinary {LH} test positivity, and a transvaginal ultrasound was performed on day cycle day 7, 10, 12 and 14/15 to investigate the growth of one dominant follicle. If a follicle reached 15 mm in diameter, an ultrasound examination was carried out every day consecutively until ovulation. On the day ovulation was detected, serum progesterone was measured to confirm the results of the ultrasound. The performance of {femSense}® was evaluated by comparing the day of ovulation confirmation with the results of ovulation prediction ({LH} test) and detection (transvaginal ultrasound). + + + Results + + The {femSense}® system confirmed ovulation occurrence in 60 cases (81.1\%) compared to 48 predicted cases (64.9\%) with the {LH} test ( + p +  = 0.041). Subgroup analysis revealed a positive trend for the {femSense}® system of specific ovulation confirmation within the fertile window of 24 h after ovulation in 42 of 74 cases (56.8\%). Cycle length, therapy method or infertility reason of the patient did not influence accuracy of the {femSense}® system. + + + + Conclusions + The {femSense}® system poses a promising alternative to the traditional {BBT} method and is a valuable surrogate marker to transvaginal ultrasound for confirmation of ovulation.}, + pages = {930010}, + journaltitle = {Front. Digit. Health}, + author = {Weiss, Gregor and Strohmayer, Karl and Koele, Werner and Reinschissler, Nina and Schenk, Michael}, + urldate = {2025-07-02}, + date = {2022-09-19}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/C3IU2JVF/Weiss et al. - 2022 - Confirmation of human ovulation in assisted reproduction using an adhesive axillary thermometer (fem.pdf:application/pdf}, +} + +@article{moghissi_accuracy_1976, + title = {Accuracy of Basal Body Temperature for Ovulation Detection}, + volume = {27}, + issn = {00150282}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0015028216422570}, + doi = {10.1016/S0015-0282(16)42257-0}, + pages = {1415--1421}, + number = {12}, + journaltitle = {Fertility and Sterility}, + author = {Moghissi, Kamran S.}, + urldate = {2025-07-02}, + date = {1976-12}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/GKF98BHR/Moghissi - 1976 - Accuracy of Basal Body Temperature for Ovulation Detection.pdf:application/pdf}, +} + +@article{bauman_basal_1981, + title = {Basal Body Temperature: Unreliable Method of Ovulation Detection}, + volume = {36}, + issn = {00150282}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0015028216459169}, + doi = {10.1016/S0015-0282(16)45916-9}, + shorttitle = {Basal Body Temperature}, + pages = {729--733}, + number = {6}, + journaltitle = {Fertility and Sterility}, + author = {Bauman, Joan E.}, + urldate = {2025-07-01}, + date = {1981-12}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/9T7V8ACT/Bauman - 1981 - Basal Body Temperature Unreliable Method of Ovulation Detection.pdf:application/pdf}, +} + +@article{luo_detection_2020, + title = {Detection and Prediction of Ovulation From Body Temperature Measured by an In-Ear Wearable Thermometer}, + volume = {67}, + rights = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/{IEEE}.html}, + issn = {0018-9294, 1558-2531}, + url = {https://ieeexplore.ieee.org/document/8715448/}, + doi = {10.1109/TBME.2019.2916823}, + abstract = {Objective: We present a non-invasive wearable device for fertility monitoring and propose an effective and flexible statistical learning algorithm to detect and predict ovulation using data captured by this device. Methods: The system consists of an earpiece, which measures the ear canal temperature every 5 minutes during night sleep hours, and a base station that transmits data to a smartphone application for analysis. We establish a data-cleaning protocol for data preprocessing and then fit a Hidden Markov Model ({HMM}) with two hidden states of high and low temperature to identify the more probable state of each time point via the predicted probabilities. Finally, a post-processing procedure is developed to incorporate biorhythm information to form a time-course biphasic profile for each subject. Results: The performance of the proposed algorithms applied to data collected by the device are compared with traditional methods in terms of match rate with self-reported ovulation days confirmed with an Ovulation Test Kit. Empirical study results from a group of 34 users yielded significant improvements over the traditional methods in terms of detection accuracy (with sensitivity 92.31\%) and prediction power (23.0731.55\% higher). Conclusion: We demonstrated the feasibility for reliable ovulation detection and prediction with high-frequency temperature data collected by a non-invasive wearable device. Significance: Traditional fertility monitoring methods are often either inaccurate or inconvenient. The wearable device and learning algorithm presented in this paper provides a userfriendly and reliable platform for tracking ovulation, which may have a broad impact on both fertility research and real-world family planning.}, + pages = {512--522}, + number = {2}, + journaltitle = {{IEEE} Trans. Biomed. Eng.}, + author = {Luo, Lan and She, Xichen and Cao, Jiexuan and Zhang, Yunlong and Li, Yijiang and Song, Peter X. K.}, + urldate = {2025-07-01}, + date = {2020-02}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/YLIR4YNX/Luo et al. - 2020 - Detection and Prediction of Ovulation From Body Temperature Measured by an In-Ear Wearable Thermomet.pdf:application/pdf}, +} + +@article{alliende_cervicovaginal_2005, + title = {Cervicovaginal fluid changes to detect ovulation accurately}, + volume = {193}, + rights = {https://www.elsevier.com/tdm/userlicense/1.0/}, + issn = {00029378}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0002937804018770}, + doi = {10.1016/j.ajog.2004.11.006}, + abstract = {Objective: The purpose of this study was to evaluate changes in cervicovaginal fluid characteristics to identify ovulation. Study design: Several ovulation indicators were studied in a university-based natural family planning center. Fifteen parous women during 29 ovulatory cycles detected cervicovaginal fluid at the vulva. They self-aspirated their upper vaginal fluid, described it, and kept it for later checking. They also took basal body temperature, collected timed first morning urine samples for estrone and pregnanediol glucuronide enzyme immunoassays, and submitted to serial ovarian transvaginal ultrasound scans. +Results: Considering a G 1-day period since ultrasound ovulation detection or allowing an extra day (ÿ1 to C2), women perceived ovulation from cervicovaginal fluid at the vulva in 76\% or 97\% of cycles, on the basis of their visual description of vaginally extracted fluid in 76\% or 90\%, which rose to 90\% or 97\% for the instructor’s description, and in 76\% or 86\% with a rapid drop in glucuronide ratio. Basal body temperature was less precise (71\% or 79\%). +Conclusion: Evaluation of cervicovaginal fluid changes is an accurate ovulation indicator. Ó 2005 Elsevier Inc. All rights reserved.}, + pages = {71--75}, + number = {1}, + journaltitle = {American Journal of Obstetrics and Gynecology}, + author = {Alliende, María Elena and Cabezón, Carlos and Figueroa, Horacio and Kottmann, Cristián}, + urldate = {2025-07-01}, + date = {2005-07}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/NN29EZ6G/Alliende et al. - 2005 - Cervicovaginal fluid changes to detect ovulation accurately.pdf:application/pdf}, +} + +@online{noauthor_ringpng_nodate, + title = {ring.png (800×800)}, + url = {https://ovularing.com/wp-content/uploads/2021/07/ring.png}, + urldate = {2025-06-25}, +} + +@online{noauthor_ovularing_nodate, + title = {{OvulaRing} Startseite}, + url = {https://ovularing.com/}, + abstract = {Erfahre hier mehr zu {OvulaRing} Startseite}, + titleaddon = {{OvulaRing}}, + urldate = {2025-06-25}, + langid = {german}, + file = {Snapshot:/home/alex/Zotero/storage/PD5DBIS4/ovularing.com.html:text/html}, +} + +@article{wu_deep_nodate, + title = {Deep Transformer Models for Time Series Forecasting:The Influenza Prevalence Case}, + abstract = {In this paper, we present a new approach to time series forecasting. Time series data are prevalent in many scientific and engineering disciplines. Time series forecasting is a crucial task in modeling time series data, and is an important area of machine learning. In this work we developed a novel method that employs Transformer-based machine learning models to forecast time series data. This approach works by leveraging selfattention mechanisms to learn complex patterns and dynamics from time series data. Moreover, it is a generic framework and can be applied to univariate and multivariate time series data, as well as time series embeddings. Using influenzalike illness ({ILI}) forecasting as a case study, we show that the forecasting results produced by our approach are favorably comparable to the stateof-the-art.}, + author = {Wu, Neo and Green, Bradley and Ben, Xue and O'Banion, Shawn}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/GHT5UMNX/Wu et al. - Deep Transformer Models for Time Series ForecastingThe Influenza Prevalence Case.pdf:application/pdf}, +} + +@article{vaswani_attention_nodate, + title = {Attention Is All You Need}, + abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 {BLEU} on the {WMT} 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 {BLEU}. On the {WMT} 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art {BLEU} score of 41.8 after training for 3.5 days on eight {GPUs}, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.}, + author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, Łukasz and Polosukhin, Illia}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/TES5P5PX/Vaswani et al. - Attention Is All You Need.pdf:application/pdf}, +} + +@article{twenge_declines_2017, + title = {Declines in Sexual Frequency among American Adults, 1989–2014}, + volume = {46}, + issn = {0004-0002, 1573-2800}, + url = {http://link.springer.com/10.1007/s10508-017-0953-1}, + doi = {10.1007/s10508-017-0953-1}, + pages = {2389--2401}, + number = {8}, + journaltitle = {Arch Sex Behav}, + author = {Twenge, Jean M. and Sherman, Ryne A. and Wells, Brooke E.}, + urldate = {2025-06-18}, + date = {2017-11}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/7FLDCF3U/Twenge et al. - 2017 - Declines in Sexual Frequency among American Adults, 1989–2014.pdf:application/pdf}, +} + +@artwork{chevalier_english_2018, + title = {English: Schematic of the Long-Short Term Memory cell, a component of recurrent neural networks}, + url = {https://commons.wikimedia.org/wiki/File:LSTM_Cell.svg}, + shorttitle = {English}, + author = {Chevalier, Guillaume}, + urldate = {2025-06-11}, + date = {2018-05-16}, + file = {Wikimedia Snapshot:/home/alex/Zotero/storage/NMYA4ZA3/FileLSTM_Cell.html:text/html}, +} + +@artwork{fdeloche_english_2017, + title = {English: A diagram for a one-unit recurrent neural network ({RNN}). From bottom to top : input state, hidden state, output state. U, V, W are the weights of the network. Compressed diagram on the left and the unfold version of it on the right.}, + url = {https://commons.wikimedia.org/wiki/File:Recurrent_neural_network_unfold.svg}, + shorttitle = {English}, + author = {{fdeloche}}, + urldate = {2025-06-11}, + date = {2017-06-19}, + file = {Wikimedia Snapshot:/home/alex/Zotero/storage/RU2XSRZN/FileRecurrent_neural_network_unfold.html:text/html}, +} + +@article{hochreiter_vanishing_1998, + title = {The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions}, + volume = {06}, + issn = {0218-4885, 1793-6411}, + url = {https://www.worldscientific.com/doi/abs/10.1142/S0218488598000094}, + doi = {10.1142/S0218488598000094}, + abstract = {Recurrent nets are in principle capable to store past inputs to produce the currently desired output. Because of this property recurrent nets are used in time series prediction and process control. Practical applications involve temporal dependencies spanning many time steps, e.g. between relevant inputs and desired outputs. In this case, however, gradient based learning methods take too much time. The extremely increased learning time arises because the error vanishes as it gets propagated back. In this article the de-caying error flow is theoretically analyzed. Then methods trying to overcome vanishing gradients are briefly discussed. Finally, experiments comparing conventional algorithms and alternative methods are presented. With advanced methods long time lag problems can be solved in reasonable time.}, + pages = {107--116}, + number = {2}, + journaltitle = {Int. J. Unc. Fuzz. Knowl. Based Syst.}, + author = {Hochreiter, Sepp}, + urldate = {2025-06-11}, + date = {1998-04}, + langid = {english}, +} + +@book{medsker_recurrent_1999, + title = {Recurrent Neural Networks: Design and Applications}, + isbn = {978-1-4200-4917-6}, + shorttitle = {Recurrent Neural Networks}, + abstract = {With existent uses ranging from motion detection to music synthesis to financial forecasting, recurrent neural networks have generated widespread attention. The tremendous interest in these networks drives Recurrent Neural Networks: Design and Applications, a summary of the design, applications, current research, and challenges of this subfield of artificial neural networks.This overview incorporates every aspect of recurrent neural networks. It outlines the wide variety of complex learning techniques and associated research projects. Each chapter addresses architectures, from fully connected to partially connected, including recurrent multilayer feedforward. It presents problems involving trajectories, control systems, and robotics, as well as {RNN} use in chaotic systems. The authors also share their expert knowledge of ideas for alternate designs and advances in theoretical aspects.The dynamical behavior of recurrent neural networks is useful for solving problems in science, engineering, and business. This approach will yield huge advances in the coming years. Recurrent Neural Networks illuminates the opportunities and provides you with a broad view of the current events in this rich field.}, + pagetotal = {414}, + publisher = {{CRC} Press}, + author = {Medsker, Larry and Jain, Lakhmi C.}, + date = {1999-12-20}, + langid = {english}, + note = {Google-Books-{ID}: {ME}1SAkN0PyMC}, + keywords = {Computers / Computer Engineering, Computers / General, Computers / Software Development \& Engineering / Systems Analysis \& Design, Technology \& Engineering / Electronics / General}, } @article{hochreiter_long_1997, - title = {Long {Short}-{Term} {Memory}}, + title = {Long Short-Term Memory}, volume = {9}, issn = {0899-7667, 1530-888X}, url = {https://direct.mit.edu/neco/article/9/8/1735-1780/6109}, doi = {10.1162/neco.1997.9.8.1735}, - abstract = {Learningtostoreinformationoverextendedtimeintervalsviarecurrentbackpropagation takesaverylongtime,mostlyduetoinsu cient,decayingerrorbackow.Webrieyreview Hochreiter's1991analysisofthisproblem,thenaddressitbyintroducinganovel,e cient, gradient-basedmethodcalled{\textbackslash}LongShort-TermMemory"(LSTM).Truncatingthegradient wherethisdoesnotdoharm,LSTMcanlearntobridgeminimaltimelagsinexcessof1000 discretetimestepsbyenforcingconstanterrorowthrough{\textbackslash}constanterrorcarrousels"within specialunits.Multiplicativegateunitslearntoopenandcloseaccesstotheconstanterror ow.LSTMislocalinspaceandtime;itscomputationalcomplexitypertimestepandweight isO(1).Ourexperimentswitharticialdatainvolvelocal,distributed,real-valued,andnoisy patternrepresentations.IncomparisonswithRTRL,BPTT,RecurrentCascade-Correlation, Elmannets,andNeuralSequenceChunking,LSTMleadstomanymoresuccessfulruns,and learnsmuchfaster.LSTMalsosolvescomplex,articiallongtimelagtasksthathavenever beensolvedbypreviousrecurrentnetworkalgorithms.}, - language = {en}, - number = {8}, - urldate = {2024-10-10}, - journal = {Neural Computation}, - author = {Hochreiter, Sepp and Schmidhuber, Jürgen}, - month = nov, - year = {1997}, + abstract = {Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory ({LSTM}). Truncating the gradient where this does not do harm, {LSTM} can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. {LSTM} is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, {LSTM} leads to many more successful runs, and learns much faster. {LSTM} also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.}, pages = {1735--1780}, - file = {PDF:/home/alex/Zotero/storage/CZSV2ASE/Hochreiter and Schmidhuber - 1997 - Long Short-Term Memory.pdf:application/pdf}, + number = {8}, + journaltitle = {Neural Computation}, + author = {Hochreiter, Sepp and Schmidhuber, Jürgen}, + urldate = {2025-06-11}, + date = {1997-11-01}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/5IE5G9KY/Hochreiter and Schmidhuber - 1997 - Long Short-Term Memory.pdf:application/pdf}, } -@misc{lim_temporal_2020, - title = {Temporal {Fusion} {Transformers} for {Interpretable} {Multi}-horizon {Time} {Series} {Forecasting}}, - url = {http://arxiv.org/abs/1912.09363}, - abstract = {Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i.e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed historically -- without any prior information on how they interact with the target. While several deep learning models have been proposed for multi-step prediction, they typically comprise black-box models which do not account for the full range of inputs present in common scenarios. In this paper, we introduce the Temporal Fusion Transformer (TFT) -- a novel attention-based architecture which combines high-performance multi-horizon forecasting with interpretable insights into temporal dynamics. To learn temporal relationships at different scales, the TFT utilizes recurrent layers for local processing and interpretable self-attention layers for learning long-term dependencies. The TFT also uses specialized components for the judicious selection of relevant features and a series of gating layers to suppress unnecessary components, enabling high performance in a wide range of regimes. On a variety of real-world datasets, we demonstrate significant performance improvements over existing benchmarks, and showcase three practical interpretability use-cases of TFT.}, - urldate = {2024-10-10}, - publisher = {arXiv}, - author = {Lim, Bryan and Arik, Sercan O. and Loeff, Nicolas and Pfister, Tomas}, - month = sep, - year = {2020}, - note = {arXiv:1912.09363}, - keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, - file = {Preprint PDF:/home/alex/Zotero/storage/2R2H34KB/Lim et al. - 2020 - Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/ETDAYW36/1912.html:text/html}, +@article{salles_softed_2024, + title = {{SoftED}: Metrics for soft evaluation of time series event detection}, + volume = {198}, + issn = {03608352}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0360835224008507}, + doi = {10.1016/j.cie.2024.110728}, + shorttitle = {{SoftED}}, + abstract = {Time series event detectors are evaluated mainly by standard classification metrics, focusing solely on detection accuracy. However, inaccuracy in detecting an event can often result from its preceding or delayed effects reflected in neighboring detections. These detections are valuable to trigger necessary actions or help mitigate unwelcome consequences. In this context, current metrics are insufficient and inadequate for the context of event detection. There is a demand for metrics that incorporate both the concept of time and temporal tolerance for neighboring detections. Inspired by fuzzy sets, this paper introduces {SoftED} metrics, a new set designed for soft evaluating event detectors. They enable the evaluation of the detection accuracy and the degree to which their detections represent events. A new general protocol inspired by competency questions is also introduced to evaluate temporal tolerant metrics for event detection. The {SoftED} metrics can improve event detection evaluations by associating events and their representative detections, incorporating temporal tolerance in over 36\% of the overall detector evaluations compared to the usual classification metrics. Following the proposed evaluation protocol, {SoftED} metrics were evaluated by domain specialists who indicated their contribution to detection evaluation and method selection.}, + pages = {110728}, + journaltitle = {Computers \& Industrial Engineering}, + author = {Salles, Rebecca and Lima, Janio and Reis, Michel and Coutinho, Rafaelli and Pacitti, Esther and Masseglia, Florent and Akbarinia, Reza and Chen, Chao and Garibaldi, Jonathan and Porto, Fabio and Ogasawara, Eduardo}, + urldate = {2025-06-03}, + date = {2024-12}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/6RYA5HIQ/Salles et al. - 2024 - SoftED Metrics for soft evaluation of time series event detection.pdf:application/pdf}, } -@misc{nie_time_2023, - title = {A {Time} {Series} is {Worth} 64 {Words}: {Long}-term {Forecasting} with {Transformers}}, - shorttitle = {A {Time} {Series} is {Worth} 64 {Words}}, - url = {http://arxiv.org/abs/2211.14730}, - abstract = {We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components: (i) segmentation of time series into subseries-level patches which are served as input tokens to Transformer; (ii) channel-independence where each channel contains a single univariate time series that shares the same embedding and Transformer weights across all the series. Patching design naturally has three-fold benefit: local semantic information is retained in the embedding; computation and memory usage of the attention maps are quadratically reduced given the same look-back window; and the model can attend longer history. Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models. We also apply our model to self-supervised pre-training tasks and attain excellent fine-tuning performance, which outperforms supervised training on large datasets. Transferring of masked pre-trained representation on one dataset to others also produces SOTA forecasting accuracy. Code is available at: https://github.com/yuqinie98/PatchTST.}, - urldate = {2024-10-10}, - publisher = {arXiv}, - author = {Nie, Yuqi and Nguyen, Nam H. and Sinthong, Phanwadee and Kalagnanam, Jayant}, - month = mar, - year = {2023}, - note = {arXiv:2211.14730}, - keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, - file = {Preprint PDF:/home/alex/Zotero/storage/DG4ZJCWV/Nie et al. - 2023 - A Time Series is Worth 64 Words Long-term Forecasting with Transformers.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/H6XGVBY6/2211.html:text/html}, -} - -@misc{shao_exploring_2023, - title = {Exploring {Progress} in {Multivariate} {Time} {Series} {Forecasting}: {Comprehensive} {Benchmarking} and {Heterogeneity} {Analysis}}, - shorttitle = {Exploring {Progress} in {Multivariate} {Time} {Series} {Forecasting}}, - url = {http://arxiv.org/abs/2310.06119}, - abstract = {Multivariate Time Series (MTS) widely exists in real-word complex systems, such as traffic and energy systems, making their forecasting crucial for understanding and influencing these systems. Recently, deep learning-based approaches have gained much popularity for effectively modeling temporal and spatial dependencies in MTS, specifically in Long-term Time Series Forecasting (LTSF) and Spatial-Temporal Forecasting (STF). However, the fair benchmarking issue and the choice of technical approaches have been hotly debated in related work. Such controversies significantly hinder our understanding of progress in this field. Thus, this paper aims to address these controversies to present insights into advancements achieved. To resolve benchmarking issues, we introduce BasicTS, a benchmark designed for fair comparisons in MTS forecasting. BasicTS establishes a unified training pipeline and reasonable evaluation settings, enabling an unbiased evaluation of over 30 popular MTS forecasting models on more than 18 datasets. Furthermore, we highlight the heterogeneity among MTS datasets and classify them based on temporal and spatial characteristics. We further prove that neglecting heterogeneity is the primary reason for generating controversies in technical approaches. Moreover, based on the proposed BasicTS and rich heterogeneous MTS datasets, we conduct an exhaustive and reproducible performance and efficiency comparison of popular models, providing insights for researchers in selecting and designing MTS forecasting models.}, - urldate = {2024-10-10}, - publisher = {arXiv}, - author = {Shao, Zezhi and Wang, Fei and Xu, Yongjun and Wei, Wei and Yu, Chengqing and Zhang, Zhao and Yao, Di and Jin, Guangyin and Cao, Xin and Cong, Gao and Jensen, Christian S. and Cheng, Xueqi}, - month = oct, - year = {2023}, - note = {arXiv:2310.06119}, - keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, - file = {Preprint PDF:/home/alex/Zotero/storage/7EFZ5IT6/Shao et al. - 2023 - Exploring Progress in Multivariate Time Series Forecasting Comprehensive Benchmarking and Heterogen.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/W6RNWBLM/2310.html:text/html}, -} - -@article{zhang_crossformer_2023, - title = {{CROSSFORMER}: {TRANSFORMER} {UTILIZING} {CROSS}- {DIMENSION} {DEPENDENCY} {FOR} {MULTIVARIATE} {TIME} {SERIES} {FORECASTING}}, - abstract = {Recently many deep models have been proposed for multivariate time series (MTS) forecasting. In particular, Transformer-based models have shown great potential because they can capture long-term dependency. However, existing Transformerbased models mainly focus on modeling the temporal dependency (cross-time dependency) yet often omit the dependency among different variables (crossdimension dependency), which is critical for MTS forecasting. To fill the gap, we propose Crossformer, a Transformer-based model utilizing cross-dimension dependency for MTS forecasting. In Crossformer, the input MTS is embedded into a 2D vector array through the Dimension-Segment-Wise (DSW) embedding to preserve time and dimension information. Then the Two-Stage Attention (TSA) layer is proposed to efficiently capture the cross-time and cross-dimension dependency. Utilizing DSW embedding and TSA layer, Crossformer establishes a Hierarchical Encoder-Decoder (HED) to use the information at different scales for the final forecasting. Extensive experimental results on six real-world datasets show the effectiveness of Crossformer against previous state-of-the-arts.}, - language = {en}, - author = {Zhang, Yunhao and Yan, Junchi}, - year = {2023}, - file = {PDF:/home/alex/Zotero/storage/NM9CETJS/Zhang and Yan - 2023 - CROSSFORMER TRANSFORMER UTILIZING CROSS- DIMENSION DEPENDENCY FOR MULTIVARIATE TIME SERIES FORECAST.pdf:application/pdf}, -} - -@misc{noauthor_pytorch-forecasting_2024, - title = {Pytorch-{Forecasting}}, - url = {https://pytorch-forecasting.readthedocs.io/en/stable/}, - urldate = {2024-10-15}, - year = {2024}, -} - -@misc{liang_foundation_2024, - title = {Foundation {Models} for {Time} {Series} {Analysis}: {A} {Tutorial} and {Survey}}, - shorttitle = {Foundation {Models} for {Time} {Series} {Analysis}}, - url = {http://arxiv.org/abs/2403.14735}, - abstract = {Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications. Recent advances in Foundation Models (FMs) have fundamentally reshaped the paradigm of model design for time series analysis, boosting various downstream tasks in practice. These innovative approaches often leverage pre-trained or fine-tuned FMs to harness generalized knowledge tailored for time series analysis. This survey aims to furnish a comprehensive and up-to-date overview of FMs for time series analysis. While prior surveys have predominantly focused on either application or pipeline aspects of FMs in time series analysis, they have often lacked an in-depth understanding of the underlying mechanisms that elucidate why and how FMs benefit time series analysis. To address this gap, our survey adopts a methodology-centric classification, delineating various pivotal elements of time-series FMs, including model architectures, pre-training techniques, adaptation methods, and data modalities. Overall, this survey serves to consolidate the latest advancements in FMs pertinent to time series analysis, accentuating their theoretical underpinnings, recent strides in development, and avenues for future exploration.}, - urldate = {2024-10-16}, - publisher = {arXiv}, - author = {Liang, Yuxuan and Wen, Haomin and Nie, Yuqi and Jiang, Yushan and Jin, Ming and Song, Dongjin and Pan, Shirui and Wen, Qingsong}, - month = jun, - year = {2024}, - note = {arXiv:2403.14735}, - keywords = {Computer Science - Machine Learning}, - file = {Preprint PDF:/home/alex/Zotero/storage/YEA28FMJ/Liang et al. - 2024 - Foundation Models for Time Series Analysis A Tutorial and Survey.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/9SGH3AIN/2403.html:text/html}, -} - -@misc{goswami_moment_2024, - title = {{MOMENT}: {A} {Family} of {Open} {Time}-series {Foundation} {Models}}, - shorttitle = {{MOMENT}}, - url = {http://arxiv.org/abs/2402.03885}, - abstract = {We introduce MOMENT, a family of open-source foundation models for general-purpose time series analysis. Pre-training large models on time series data is challenging due to (1) the absence of a large and cohesive public time series repository, and (2) diverse time series characteristics which make multi-dataset training onerous. Additionally, (3) experimental benchmarks to evaluate these models, especially in scenarios with limited resources, time, and supervision, are still in their nascent stages. To address these challenges, we compile a large and diverse collection of public time series, called the Time series Pile, and systematically tackle time series-specific challenges to unlock large-scale multi-dataset pre-training. Finally, we build on recent work to design a benchmark to evaluate time series foundation models on diverse tasks and datasets in limited supervision settings. Experiments on this benchmark demonstrate the effectiveness of our pre-trained models with minimal data and task-specific fine-tuning. Finally, we present several interesting empirical observations about large pre-trained time series models. Pre-trained models (AutonLab/MOMENT-1-large) and Time Series Pile (AutonLab/Timeseries-PILE) are available on Huggingface.}, - urldate = {2024-10-16}, - publisher = {arXiv}, - author = {Goswami, Mononito and Szafer, Konrad and Choudhry, Arjun and Cai, Yifu and Li, Shuo and Dubrawski, Artur}, - month = oct, - year = {2024}, - note = {arXiv:2402.03885}, - keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, - file = {Preprint PDF:/home/alex/Zotero/storage/QF6E6J8W/Goswami et al. - 2024 - MOMENT A Family of Open Time-series Foundation Models.pdf:application/pdf}, -} - -@misc{noauthor_decoder-only_nodate, - title = {A decoder-only foundation model for time-series forecasting}, - url = {http://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/}, - abstract = {Posted by Rajat Sen and Yichen Zhou, Google Research Time-series forecasting is ubiquitous in various domains, such as retail, finance, manufacturi...}, - language = {en}, - urldate = {2024-10-16}, - file = {Snapshot:/home/alex/Zotero/storage/JV9JIF73/a-decoder-only-foundation-model-for-time-series-forecasting.html:text/html}, -} - -@misc{shi_time-moe_2024, - title = {Time-{MoE}: {Billion}-{Scale} {Time} {Series} {Foundation} {Models} with {Mixture} of {Experts}}, - shorttitle = {Time-{MoE}}, - url = {http://arxiv.org/abs/2409.16040}, - abstract = {Deep learning for time series forecasting has seen significant advancements over the past decades. However, despite the success of large-scale pre-training in language and vision domains, pre-trained time series models remain limited in scale and operate at a high cost, hindering the development of larger capable forecasting models in real-world applications. In response, we introduce Time-MoE, a scalable and unified architecture designed to pre-train larger, more capable forecasting foundation models while reducing inference costs. By leveraging a sparse mixture-of-experts (MoE) design, Time-MoE enhances computational efficiency by activating only a subset of networks for each prediction, reducing computational load while maintaining high model capacity. This allows Time-MoE to scale effectively without a corresponding increase in inference costs. Time-MoE comprises a family of decoder-only transformer models that operate in an auto-regressive manner and support flexible forecasting horizons with varying input context lengths. We pre-trained these models on our newly introduced large-scale data Time-300B, which spans over 9 domains and encompassing over 300 billion time points. For the first time, we scaled a time series foundation model up to 2.4 billion parameters, achieving significantly improved forecasting precision. Our results validate the applicability of scaling laws for training tokens and model size in the context of time series forecasting. Compared to dense models with the same number of activated parameters or equivalent computation budgets, our models consistently outperform them by large margin. These advancements position Time-MoE as a state-of-the-art solution for tackling real-world time series forecasting challenges with superior capability, efficiency, and flexibility.}, - urldate = {2024-10-16}, - publisher = {arXiv}, - author = {Shi, Xiaoming and Wang, Shiyu and Nie, Yuqi and Li, Dianqi and Ye, Zhou and Wen, Qingsong and Jin, Ming}, - month = oct, - year = {2024}, - note = {arXiv:2409.16040}, - keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, - file = {Preprint PDF:/home/alex/Zotero/storage/49N63CMZ/Shi et al. - 2024 - Time-MoE Billion-Scale Time Series Foundation Models with Mixture of Experts.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/HPHN7WNJ/2409.html:text/html}, -} - -@article{coninck_dianne_2018, - title = {{DIANNE}: a modular framework for designing, training and deploying deep neural networks on heterogeneous distributed infrastructure}, - volume = {141}, - issn = {01641212}, - shorttitle = {{DIANNE}}, - url = {https://linkinghub.elsevier.com/retrieve/pii/S0164121218300487}, - doi = {10.1016/j.jss.2018.03.032}, - language = {en}, - urldate = {2024-10-23}, - journal = {Journal of Systems and Software}, - author = {Coninck, Elias De and Bohez, Steven and Leroux, Sam and Verbelen, Tim and Vankeirsbilck, Bert and Simoens, Pieter and Dhoedt, Bart}, - month = jul, - year = {2018}, - pages = {52--65}, - file = {Full Text:/home/alex/Zotero/storage/SFH656QN/Coninck et al. - 2018 - DIANNE a modular framework for designing, training and deploying deep neural networks on heterogene.pdf:application/pdf}, -} - -@misc{noauthor_keras_nodate, - title = {Keras: {Deep} {Learning} for humans}, - url = {https://keras.io/}, - urldate = {2024-10-23}, - file = {Keras\: Deep Learning for humans:/home/alex/Zotero/storage/MS4QLPRC/keras.io.html:text/html}, -} - -@article{masuda_machine_2025, - title = {Machine learning model for menstrual cycle phase classification and ovulation day detection based on sleeping heart rate under free-living conditions}, - volume = {187}, - issn = {0010-4825}, - url = {https://www.sciencedirect.com/science/article/pii/S0010482525000551}, - doi = {10.1016/j.compbiomed.2025.109705}, - abstract = {The accurate classification of menstrual cycle phases and detection of ovulation is critical for women's health management, particularly in addressing infertility, alleviating premenstrual syndrome, and preventing hormone-related disorders. However, traditional basal body temperature (BBT) measurement methods are susceptible to disruptions in sleep timing and environmental conditions, limiting practical application. This study is aimed to overcome these limitations by introducing a novel feature, heart rate at the circadian rhythm nadir (minHR), for classifying menstrual cycle phases and predicting ovulation. A machine learning model was developed using XGBoost, and data were collected under free-living conditions from 40 healthy women (18–34 years) over a maximum of three menstrual cycles. Three feature combinations— “day,” “day + minHR,” and “day + BBT”—were evaluated, and model performance was assessed using nested leave-one-group-out cross-validation. The feature “day” represents the number of days elapsed since the onset of menstruation. Participants were stratified into groups depending on high variability and low variability in sleep timing. Results demonstrated that adding minHR significantly improved luteal phase classification and ovulation day detection performance compared to “day” only. Furthermore, in participants with high variability in sleep timing, the minHR-based model outperformed the BBT-based model, significantly improving luteal phase recall and reducing ovulation day detection absolute errors by 2 d (p {\textless} 0.05). These findings highlight the robustness and practicality of the minHR-based model for menstrual cycle tracking, particularly in individuals with high variability in sleep timing. The proposed model holds great promise for personalized health management and large-scale epidemiological research.}, - urldate = {2025-02-11}, - journal = {Computers in Biology and Medicine}, - author = {Masuda, Hazuki and Okada, Shima and Shiozawa, Naruhiro and Sakaue, Yusuke and Manno, Masanobu and Makikawa, Masaaki and Isaka, Tadao}, - month = mar, - year = {2025}, - keywords = {Circadian rhythm, Heart rate, Machine learning, Menstrual cycle tracking, Ovulation day detection, Wearable sensor, XGBoost}, - pages = {109705}, - file = {PDF:/home/alex/Zotero/storage/87H6TB3Q/Masuda et al. - 2025 - Machine learning model for menstrual cycle phase classification and ovulation day detection based on.pdf:application/pdf;ScienceDirect Snapshot:/home/alex/Zotero/storage/PC6FSQIA/S0010482525000551.html:text/html}, -} - -@article{maman_prediction_2023, - title = {Prediction of ovulation: new insight into an old challenge}, - volume = {13}, - issn = {2045-2322}, - shorttitle = {Prediction of ovulation}, - url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10651856/}, - doi = {10.1038/s41598-023-47241-2}, - abstract = {Ultrasound monitoring and hormonal blood testing are considered by many as an accurate method to predict ovulation time. However, uniform and validated algorithms for predicting ovulation have yet to be defined. Daily hormonal tests and transvaginal ultrasounds were recorded to develop an algorithm for ovulation prediction. The rupture of the leading ovarian follicle was a marker for ovulation day. The model was validated retrospectively on natural cycles frozen embryo transfer cycles with documented ovulation. Circulating levels of LH or its relative variation failed, by themselves, to reliably predict ovulation. Any decrease in estrogen was 100\% associated with ovulation emergence the same day or the next day. Progesterone levels {\textgreater} 2 nmol/L had low specificity to predict ovulation the next day (62.7\%), yet its sensitivity was high (91.5\%). A model for ovulation prediction, combining the three hormone levels and ultrasound was created with an accuracy of 95\% to 100\% depending on the combination of the hormone levels. Model validation showed correct ovulation prediction in 97\% of these cycles. We present an accurate ovulation prediction algorithm. The algorithm is simple and user-friendly so both reproductive endocrinologists and general practitioners can use it to benefit their patients.}, - urldate = {2025-02-11}, - journal = {Scientific Reports}, - author = {Maman, Ettie and Adashi, Eli Y. and Baum, Micha and Hourvitz, Ariel}, - month = nov, - year = {2023}, - pmid = {37968377}, - pmcid = {PMC10651856}, - pages = {20003}, - annote = {also not “really” relevant, as domain is ultrasound and hormone levels - -}, - file = {PubMed Central Full Text PDF:/home/alex/Zotero/storage/MTHDPZ5B/Maman et al. - 2023 - Prediction of ovulation new insight into an old challenge.pdf:application/pdf}, -} - -@article{luo_prediction_2025, - title = {Prediction of the fertile window and menstrual cycles with a wearable device via machine-learning algorithms}, - issn = {1472-6483}, - url = {https://www.sciencedirect.com/science/article/pii/S1472648325000021}, - doi = {10.1016/j.rbmo.2025.104795}, - abstract = {Research question -We aimed to develop fertile window and menstruation prediction algorithms through machine learning based on women's physiological parameters data collected by Huawei Band 6 pro from both regular and irregular menstruators. -Design -This was a prospective observational cohort study conducted at Obstetrics and Gynecology Hospital of Fudan University. Participants were recruited from November 2021 to September 2022. Each participant wore Huawei Band 6 pro to record wrist skin temperature (WST), heart rate (HR), heart rate variability, and respiratory rate. Algorithms were developed to predict the fertile window and menstrual cycle based on WST and HR. -Results -We included data from 270 and 84 qualified cycles with confirmed ovulations from 136 regular and 47 irregular menstruators. For regular menstruators, the prediction algorithm based on WST and HR for the fertile window had an accuracy of 85.47\%, a sensitivity of 70.07\%, a specificity of 89.77\%, and AUC of 0.869. The algorithms for menstrual first day labelling and onset within 3 days gained an accuracy of 83.6\% and 75.0\%. For irregular menstruators, the accuracy, sensitivity, specificity and AUC were 79.85\%, 42.79\%, 87.28\%, and 0.763 respectively, for fertile window prediction. The accuracy of menses labelling and prediction were 61.2\%, and 50.8\% respectively. -Conclusions -Based on WST and HR data from the wearable device, the algorithms demonstrated reliable performance in predicting the fertile window and menstruation day among regular menstruators. These algorithms also showed potential for assisting irregular menstruators in managing their cycles and planning for conception.}, - urldate = {2025-02-11}, - journal = {Reproductive BioMedicine Online}, - author = {Luo, Chuan and Su, Yun-Fei and Ren, Yun-Yun and Zhang, Qin and Li, Ran and Zhang, Qi and Li, Cheng and Hao, Yan-Hui and Zhang, An-Qi and Zhang, Hao and Huang, He-Feng and Wu, Yan-Ting}, - month = jan, - year = {2025}, - keywords = {Machine learning, Fertile window, Menstrual cycle, Natural cycle, Non-invasive wearable device, Wrist skin temperature}, - pages = {104795}, - annote = { - -need closer look, how do they determine the fertile phase? what do they test against? what model do they use? - - -}, - file = {PDF:/home/alex/Zotero/storage/YIV6T8MS/Luo et al. - 2025 - Prediction of the fertile window and menstrual cycles with a wearable device via machine-learning al.pdf:application/pdf;ScienceDirect Snapshot:/home/alex/Zotero/storage/3IJMUH82/S1472648325000021.html:text/html}, -} - -@article{yu_tracking_2022, - title = {Tracking of menstrual cycles and prediction of the fertile window via measurements of basal body temperature and heart rate as well as machine-learning algorithms}, - volume = {20}, - issn = {1477-7827}, - url = {https://doi.org/10.1186/s12958-022-00993-4}, - doi = {10.1186/s12958-022-00993-4}, - abstract = {Fertility awareness and menses prediction are important for improving fecundability and health management. Previous studies have used physiological parameters, such as basal body temperature (BBT) and heart rate (HR), to predict the fertile window and menses. However, their accuracy is far from satisfactory. Additionally, few researchers have examined irregular menstruators. Thus, we aimed to develop fertile window and menstruation prediction algorithms for both regular and irregular menstruators.}, - number = {1}, - urldate = {2025-02-11}, - journal = {Reproductive Biology and Endocrinology}, - author = {Yu, Jia-Le and Su, Yun-Fei and Zhang, Chen and Jin, Li and Lin, Xian-Hua and Chen, Lu-Ting and Huang, He-Feng and Wu, Yan-Ting}, - month = aug, - year = {2022}, - keywords = {Heart rate, Machine learning, Fertile window, Menstrual cycle, Basal body temperature, Wearable device}, - pages = {118}, - annote = { - -potentially interesting - - -also use temperature plus hormone levels and ultrasounds - - -}, - file = {Full Text PDF:/home/alex/Zotero/storage/7Z8P97UF/Yu et al. - 2022 - Tracking of menstrual cycles and prediction of the fertile window via measurements of basal body tem.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/SMRBT6EG/s12958-022-00993-4.html:text/html}, -} - -@article{luz_improved_2024, - title = {Improved clinical pregnancy rates in natural frozen-thawed embryo transfer cycles with machine learning ovulation prediction: insights from a retrospective cohort study}, - volume = {14}, - copyright = {2024 The Author(s)}, - issn = {2045-2322}, - shorttitle = {Improved clinical pregnancy rates in natural frozen-thawed embryo transfer cycles with machine learning ovulation prediction}, - url = {https://www.nature.com/articles/s41598-024-80356-8}, - doi = {10.1038/s41598-024-80356-8}, - abstract = {This study aims to develop physician support software for determining ovulation time and assess its impact on pregnancy outcomes in natural cycle frozen embryo transfers (NC-FET). To develop, assess, and validate an ovulation prediction model, three datasets were used: REI Ovulation Determination dataset (500 cycles) split into training (309), validation (90), and test (101) sets; the Documented Ovulation dataset (101 cycles) with confirmed ovulation (documented follicular rupture and LH surge); and the Clinical Pregnancy Rates dataset (515 NC-FET cycles), categorized into “Matched” and “Mismatched” based on alignment with the model’s ovulation determination. Pregnancy outcomes were compared between the groups. The ovulation prediction model exhibited 93.85\% and 92.89\% matching rates with the REI Ovulation Determination and Documented Ovulation datasets, respectively. In the Clinical Pregnancy Rates dataset, the Matched group (282 cycles) showed significantly higher clinical pregnancy rates than the Mismatched group (34.6\% vs. 25.9\%, p = 0.04) and similar results for patients under 37 (41.1\% vs. 30.7\%, p = 0.04). Logistic regression indicated lower pregnancy rates in Mismatched cases (odds ratio 0.67 for the general population, 0.63 for patients under 37). In conclusion, we introduce a highly accurate AI ovulation prediction model. Treatment cycles aligning with the model’s recommendations had significantly increased clinical pregnancy rates.}, - language = {en}, - number = {1}, - urldate = {2025-02-11}, - journal = {Scientific Reports}, - author = {Luz, Almog and Hourvitz, Ariel and Moran, Eden and Itzhak, Nevo and Reuvenny, Shachar and Hourvitz, Rohi and Youngster, Michal and Baum, Micha and Maman, Ettie}, - month = nov, - year = {2024}, - note = {Publisher: Nature Publishing Group}, - keywords = {Infertility, Outcomes research}, - pages = {29451}, - file = {Full Text PDF:/home/alex/Zotero/storage/BSHNTIFD/Luz et al. - 2024 - Improved clinical pregnancy rates in natural frozen-thawed embryo transfer cycles with machine learn.pdf:application/pdf}, -} - -@article{pratikno_pdf_2024, - title = {({PDF}) {A} novel women's ovulation prediction through salivary ferning using the box counting and deep learning}, - url = {https://www.researchgate.net/publication/379467622_A_novel_women's_ovulation_prediction_through_salivary_ferning_using_the_box_counting_and_deep_learning}, - doi = {10.11591/eei.v13i2.5847}, - abstract = {PDF {\textbar} There are several methods to predict a woman's ovulation time, including using a calendar system, basal body temperature, ovulation prediction... {\textbar} Find, read and cite all the research you need on ResearchGate}, - language = {en}, - urldate = {2025-02-11}, - journal = {ResearchGate}, - author = {Pratikno and Ibrahim and Jusak}, - month = dec, - year = {2024}, - annote = {Not really relevant, as different data domain -{\textgreater} saliva -}, - file = {Full Text:/home/alex/Zotero/storage/MVSBPZQG/2024 - (PDF) A novel women's ovulation prediction through salivary ferning using the box counting and deep.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/8FYLC8YZ/379467622_A_novel_women's_ovulation_prediction_through_salivary_ferning_using_the_box_counting_.html:text/html}, -} - -@article{shkodzik_innovative_2024, - title = {Innovative {Approaches} to {Digital} {Health} in {Ovulation} {Detection}: {A} {Review} of {Current} {Methods} and {Emerging} {Technologies}}, - volume = {42}, - issn = {1526-4564}, - shorttitle = {Innovative {Approaches} to {Digital} {Health} in {Ovulation} {Detection}}, - doi = {10.1055/s-0044-1793829}, - abstract = {Ovulation is a vital sign, as significant as body temperature, heart rate, respiratory rate, and blood pressure, in assessing overall health and identifying potential health issues. Ovulation is a key event of the menstrual cycle that provides insights into the hormonal and reproductive health aspects. Affected by the orchestra of hormones, namely thyroid, prolactin, and androgens, disruptions in ovulation can indicate endocrinological conditions and lead to gynecological problems, such as heavy menstrual bleeding, irregular periods, amenorrhea, dysmenorrhea, and difficulties in getting pregnant. Monitoring ovulation and detecting disruptions can aid in the early detection of health issues, extending beyond reproductive health concerns. It can help identify underlying causes of symptoms like excessive fatigue and abnormal hair growth. The integration of digital health technologies, such as mobile apps using machine learning algorithms, wearables tracking temperature, heart rate, breath rate, and sleep patterns, and devices measuring reproductive hormones in urine or saliva samples, offers a wealth of opportunities in family planning, early health issue diagnosis, treatment adjustment, and tracking menstrual cycles during assisted reproductive techniques. These advancements provide a comprehensive approach to health monitoring, addressing both reproductive and overall health concerns.}, - language = {eng}, - number = {2}, - journal = {Seminars in Reproductive Medicine}, - author = {Shkodzik, Katerina}, - month = jun, - year = {2024}, - pmid = {39572028}, - keywords = {Humans, Digital Health, Female, Mobile Applications, Ovulation, Ovulation Detection, Telemedicine, Wearable Electronic Devices}, - pages = {81--89}, -} - -@article{lin_transformer_2023, - title = {Transformer neural network to predict and interpret pregnancy loss from activity data in {Holstein} dairy cows}, - volume = {205}, - issn = {0168-1699}, - url = {https://www.sciencedirect.com/science/article/pii/S0168169923000261}, - doi = {10.1016/j.compag.2023.107638}, - abstract = {Predicting/detecting pregnancy loss of dairy cows offers the opportunity to shorten the time interval between artificial inseminations. Although several methods of pregnancy detection are being practiced, models with accurate, timely and interpretable detection of pregnancy are still lacking. This study proposed a transformer neural network to predict the probability of pregnancy loss based on continuous activity data, which were collected from activity-monitoring tags attached to 185 Holstein cows from a commercial dairy farm in Cayuga County, NY, USA. Our best model achieved an average accuracy of 0.87, F1 score of 0.87, recall of 0.87 and specificity of 0.90 using 14-day time-series activity windows (90\% overlap) using 5-fold cross-validation, outperforming commonly used classic statistical learning and deep learning models for time-series data. The results indicated that our predictive model gave high probabilities of correctly detecting pregnancy loss prior to the increased activities and veterinary confirmation by transrectal ultrasound. In addition, our model interpretation aligned with the changes in the temporal activity levels, revealing that drastic fluctuations in time-series activity data contributed heavily to the final prediction. To the best of our knowledge, this is the first work on developing transformer models for the prediction of pregnancy loss in dairy cows. In addition to facilitating the development of future precision management on modern farms, our work potentiates an increase in the reproductive efficiency and profitability of dairy farms.}, - urldate = {2025-02-11}, - journal = {Computers and Electronics in Agriculture}, - author = {Lin, Dan and Kenéz, Ákos and McArt, Jessica A. A. and Li, Jun}, - month = feb, - year = {2023}, - keywords = {Dairy cow, Precision livestock farming, Pregnancy loss prediction, Time-series activity}, - pages = {107638}, -} - -@article{luz_p-656_2023, - title = {P-656 {Machine} learning algorithm automatically manages and accurately predicts ovulation in natural frozen-thawed embryo transfer cycles.}, - volume = {38}, - issn = {0268-1161}, - url = {https://doi.org/10.1093/humrep/dead093.983}, - doi = {10.1093/humrep/dead093.983}, - abstract = {Can an Artificial Intelligence (AI) algorithm automatically manage frozen-thawed embryo transfer (NC-FET) treatment cycles and give an accurate prediction of ovulation day.An AI algorithm automatically managed and predicted the ovulation of NC-FET treatment cycles with 94.8\% accuracy using an average of 3.01 test days.Today the preferred method for frozen embryo transfer is natural cycle based on ovulation detection. Currently, there is no software capable of managing the treatment cycle automatically and identifying the time of ovulation to support doctor decisions. The aim of this study is to develop a physician support AI software for determining ovulation time reliably with high accuracy.2083 NC-FET cycles from September 2018 to June 2021 were used to develop the ovulation detection and treatment management algorithms.Each cycle had data from at least 2 visits including: hormonal levels (Estrogen/Progesterone/LH) and follicle sizes.The dataset was divided into a train set and two test sets. In the 1st test set ovulation was determined by experts’ opinions and the 2nd test set included cycles in which follicle rupture was documented in consecutive ultrasounds.Two algorithms were developed, an ovulation prediction algorithm based on an NGBoost model and a treatment management algorithm that used the model to determine if and when to call for a new test or declare the ovulation day.Both algorithms were jointly tuned to reach the highest success rate, defined as providing the correct day of ovulation using the available cycle data, with as few test days as possible.On the first test set, which consisted of 176 cycles in which ovulation was determined through the majority decision of 2 independent experts and the attending physician, the treatment management algorithm required on average 3.01 tests to reach a prediction and successfully predicted the ovulation day in 94.8\% of cycles.In the second test set, which consisted of 29 cycles in which ovulation was determined through the follicular rupture in two consecutive ultrasounds, only the ovulation prediction model was tested. To ensure that the model provides a reliable answer and does not rely solely on the follicular disappearances, examined cycles were tested twice: Once using the ovulation day without the day prior to it, and again using only the day prior to ovulation without the ovulation day itself. The algorithm accurately predicted ovulation in 28 out of 29 instances (96.6\%) using the day of ovulation and in 28 out of 29 instances (96.6\%) using the day before ovulation.The main drawback is this being a retrospective study: while the algorithm was trained to maximize accuracy when it selects the test days, the dataset test days were selected by the attending physicians. Statistical methods were used to overcome this, however further prospective trials are needed to validate the results.This is the first AI algorithm designed to automatically manage NC-FET IVF treatment cycles and predict ovulation. The high accuracy and low average tests count might improve treatment outcomes, reduce the patients’ life disruption, and allow physicians to spend less time monitoring their patients’ treatments.not applicable}, - number = {Supplement\_1}, - urldate = {2025-02-11}, - journal = {Human Reproduction}, - author = {Luz, A and Hourvitz, R and Reuvenny, S and Youngster, M and Baum, M and Hourvitz, A and Maman, E}, - month = jun, - year = {2023}, - pages = {dead093.983}, - file = {Full Text PDF:/home/alex/Zotero/storage/C8YM765Y/Luz et al. - 2023 - P-656 Machine learning algorithm automatically manages and accurately predicts ovulation in natural.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/WANIZX3H/7202977.html:text/html}, -} - -@inproceedings{azaria_semi-supervised_2019, - title = {Semi-{Supervised} {Ovulation} {Detection} {Based} on {Multiple} {Properties}}, - url = {https://ieeexplore.ieee.org/document/8995235}, - doi = {10.1109/ICTAI.2019.00039}, - abstract = {Despite being a well-researched problem, ovulation detection in human female remains a difficult task. Most current methods for ovulation detection rely on measurements of a single property (e.g. morning body temperature) or at most on two properties (e.g. both salivary and vaginal electrical resistance). In this paper we present a machine learning based method for detecting the day in which ovulation occurs. Our method considered measurements of five different properties. We crawled a data-set from the web and showed that our method outperforms current state-of-the-art methods for ovulation detection. Our method performs well also when considering measurements of fewer properties. We show that our method's performance can be further improved by using unlabeled data, that is, mensuration cycles without a know ovulation date. Our resulted machine learning model can be very useful for women trying to conceive that have trouble in recognizing their ovulation period, especially when some measurements are missing.}, - urldate = {2025-02-11}, - booktitle = {2019 {IEEE} 31st {International} {Conference} on {Tools} with {Artificial} {Intelligence} ({ICTAI})}, - author = {Azaria, Amos and Azaria, Seagal}, - month = nov, - year = {2019}, - note = {ISSN: 2375-0197}, - keywords = {ovulation detection, semi supervised learning}, - pages = {222--228}, - annote = { - -datapoints - - -basal body temperature - - -salivary electrical resistance - - -vaginal electric resistance - - -ovulation prediction kit -{\textgreater} measures for LH hormone - - -markers such as breast tenderness, cervical mucus and custom - - - - -models - - -cnn - - -LSTM - - -conditional random fields approach? - - -“semi supervised” idea is nuts, using the model to create labels that feed back into the training process (?) - - - - -Use leave one out cross validation, -{\textgreater} questionable results - - -}, - file = {IEEE Xplore Abstract Record:/home/alex/Zotero/storage/JSAEWRGB/8995235.html:text/html;PDF:/home/alex/Zotero/storage/P74SEG9S/Azaria and Azaria - 2019 - Semi-Supervised Ovulation Detection Based on Multiple Properties.pdf:application/pdf}, -} - -@article{fanton_interpretable_2022, - title = {An interpretable machine learning model for predicting the optimal day of trigger during ovarian stimulation}, - volume = {118}, - issn = {0015-0282, 1556-5653}, - url = {https://www.fertstert.org/article/S0015-0282%2822%2900244-8/fulltext}, - doi = {10.1016/j.fertnstert.2022.04.003}, - language = {English}, - number = {1}, - urldate = {2025-02-11}, - journal = {Fertility and Sterility}, - author = {Fanton, Michael and Nutting, Veronica and Solano, Funmi and Maeder-York, Paxton and Hariton, Eduardo and Barash, Oleksii and Weckstein, Louis and Sakkas, Denny and Copperman, Alan B. and Loewke, Kevin}, - month = jul, - year = {2022}, - note = {Publisher: Elsevier}, - keywords = {Artificial intelligence, in vitro fertilization, machine learning, ovarian stimulation, trigger}, - pages = {101--108}, - file = {Full Text PDF:/home/alex/Zotero/storage/UNX7ASLP/Fanton et al. - 2022 - An interpretable machine learning model for predicting the optimal day of trigger during ovarian sti.pdf:application/pdf}, -} - -@article{braude_machine_2024, - title = {Machine learning for predicting elective fertility preservation outcomes}, - volume = {14}, - copyright = {2024 The Author(s)}, - issn = {2045-2322}, - url = {https://www.nature.com/articles/s41598-024-60671-w}, - doi = {10.1038/s41598-024-60671-w}, - abstract = {This retrospective study applied machine-learning models to predict treatment outcomes of women undergoing elective fertility preservation. Two-hundred-fifty women who underwent elective fertility preservation at a tertiary center, 2019–2022 were included. Primary outcome was the number of metaphase II oocytes retrieved. Outcome class was based on oocyte count (OC): Low (≤ 8), Medium (9–15) or High (≥ 16). Machine-learning models and statistical regression were used to predict outcome class, first based on pre-treatment parameters, and then using post-treatment data from ovulation-triggering day. OC was 136 Low, 80 Medium, and 34 High. Random Forest Classifier (RFC) was the most accurate model (pre-treatment receiver operating characteristic (ROC) area under the curve (AUC) was 77\%, and post-treatment ROC AUC was 87\%), followed by XGBoost Classifier (pre-treatment ROC AUC 74\%, post-treatment ROC AUC 86\%). The most important pre-treatment parameters for RFC were basal FSH (22.6\%), basal LH (19.1\%), AFC (18.2\%), and basal estradiol (15.6\%). Post-treatment parameters were estradiol levels on trigger-day (17.7\%), basal FSH (11\%), basal LH (9\%), and AFC (8\%). Machine-learning models trained with clinical data appear to predict fertility preservation treatment outcomes with relatively high accuracy.}, - language = {en}, - number = {1}, - urldate = {2025-02-11}, - journal = {Scientific Reports}, - author = {Braude, Itai and Haikin Herzberger, Einat and Semo, Mor and Soifer, Kim and Goren Gepstein, Nitzan and Wiser, Amir and Miller, Netanella}, - month = may, - year = {2024}, - note = {Publisher: Nature Publishing Group}, - keywords = {Outcomes research, Computational models}, - pages = {10158}, - file = {Full Text PDF:/home/alex/Zotero/storage/URDGBHLV/Braude et al. - 2024 - Machine learning for predicting elective fertility preservation outcomes.pdf:application/pdf}, -} - -@article{noauthor_temporal_2021, - title = {Temporal {Fusion} {Transformers} for interpretable multi-horizon time series forecasting}, - volume = {37}, - issn = {0169-2070}, - url = {https://www.sciencedirect.com/science/article/pii/S0169207021000637}, - doi = {10.1016/j.ijforecast.2021.03.012}, - abstract = {Multi-horizon forecasting often contains a complex mix of inputs – including static (i.e. time-invariant) covariates, known future inputs, and other e…}, - language = {en-US}, - number = {4}, - urldate = {2025-02-24}, - journal = {International Journal of Forecasting}, - month = oct, - year = {2021}, - note = {Publisher: Elsevier}, - pages = {1748--1764}, - file = {Snapshot:/home/alex/Zotero/storage/SFYESIWK/S0169207021000637.html:text/html;Submitted Version:/home/alex/Zotero/storage/A9AYS5UI/2021 - Temporal Fusion Transformers for interpretable multi-horizon time series forecasting.pdf:application/pdf}, -} - -@inproceedings{vaswani_attention_2017, - title = {Attention is {All} you {Need}}, - volume = {30}, - url = {https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html}, - abstract = {The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms. We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train. Our single model with 165 million parameters, achieves 27.5 BLEU onEnglish-to-German translation, improving over the existing best ensemble result by over 1 BLEU. On English-to-French translation, we outperform the previoussingle state-of-the-art with model by 0.7 BLEU, achieving a BLEU score of 41.1.}, - urldate = {2025-02-24}, - booktitle = {Advances in {Neural} {Information} {Processing} {Systems}}, - publisher = {Curran Associates, Inc.}, - author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, Ł ukasz and Polosukhin, Illia}, - year = {2017}, - file = {Full Text PDF:/home/alex/Zotero/storage/MU7NU9LR/Vaswani et al. - 2017 - Attention is All you Need.pdf:application/pdf}, -} - -@misc{noauthor_vivosens_nodate, - title = {vivosens medical gmbh}, - url = {https://www.vivosensmedical.com/}, - urldate = {2025-02-24}, - file = {vivosensmedical.com:/home/alex/Zotero/storage/KSM7HHBJ/www.vivosensmedical.com.html:text/html}, -} - -@inproceedings{rigotti_attention-based_2021, - title = {Attention-based {Interpretability} with {Concept} {Transformers}}, - url = {https://openreview.net/forum?id=kAa9eDS0RdO}, - abstract = {Attention is a mechanism that has been instrumental in driving remarkable performance gains of deep neural network models in a host of visual, NLP and multimodal tasks. One additional notable aspect of attention is that it conveniently exposes the ``reasoning'' behind each particular output generated by the model. Specifically, attention scores over input regions or intermediate features have been interpreted as a measure of the contribution of the attended element to the model inference. While the debate in regard to the interpretability of attention is still not settled, researchers have pointed out the existence of architectures and scenarios that afford a meaningful interpretation of the attention mechanism. Here we propose the generalization of attention from low-level input features to high-level concepts as a mechanism to ensure the interpretability of attention scores within a given application domain. In particular, we design the ConceptTransformer, a deep learning module that exposes explanations of the output of a model in which it is embedded in terms of attention over user-defined high-level concepts. Such explanations are {\textbackslash}emph\{plausible\} (i.e.{\textbackslash} convincing to the human user) and {\textbackslash}emph\{faithful\} (i.e.{\textbackslash} truly reflective of the reasoning process of the model). Plausibility of such explanations is obtained by construction by training the attention heads to conform with known relations between inputs, concepts and outputs dictated by domain knowledge. Faithfulness is achieved by design by enforcing a linear relation between the transformer value vectors that represent the concepts and their contribution to the classification log-probabilities. We validate our ConceptTransformer module on established explainability benchmarks and show how it can be used to infuse domain knowledge into classifiers to improve accuracy, and conversely to extract concept-based explanations of classification outputs. Code to reproduce our results is available at: {\textbackslash}url\{https://github.com/ibm/concept\_transformer\}.}, - language = {en}, - urldate = {2025-02-21}, - author = {Rigotti, Mattia and Miksovic, Christoph and Giurgiu, Ioana and Gschwind, Thomas and Scotton, Paolo}, - month = oct, - year = {2021}, - file = {Full Text PDF:/home/alex/Zotero/storage/U2FUGVF6/Rigotti et al. - 2021 - Attention-based Interpretability with Concept Transformers.pdf:application/pdf}, -} - -@article{kitada_attention_2021, - title = {Attention {Meets} {Perturbations}: {Robust} and {Interpretable} {Attention} {With} {Adversarial} {Training}}, - volume = {9}, - issn = {2169-3536}, - shorttitle = {Attention {Meets} {Perturbations}}, - url = {https://ieeexplore.ieee.org/abstract/document/9467291}, - doi = {10.1109/ACCESS.2021.3093456}, - abstract = {Although attention mechanisms have been applied to a variety of deep learning models and have been shown to improve the prediction performance, it has been reported to be vulnerable to perturbations to the mechanism. To overcome the vulnerability to perturbations in the mechanism, we are inspired by adversarial training (AT), which is a powerful regularization technique for enhancing the robustness of the models. In this paper, we propose a general training technique for natural language processing tasks, including AT for attention (Attention AT) and more interpretable AT for attention (Attention iAT). The proposed techniques improved the prediction performance and the model interpretability by exploiting the mechanisms with AT. In particular, Attention iAT boosts those advantages by introducing adversarial perturbation, which enhances the difference in the attention of the sentences. Evaluation experiments with ten open datasets revealed that AT for attention mechanisms, especially Attention iAT, demonstrated (1) the best performance in nine out of ten tasks and (2) more interpretable attention (i.e., the resulting attention correlated more strongly with gradient-based word importance) for all tasks. Additionally, the proposed techniques are (3) much less dependent on perturbation size in AT.}, - urldate = {2025-02-21}, - journal = {IEEE Access}, - author = {Kitada, Shunsuke and Iyatomi, Hitoshi}, - year = {2021}, - note = {Conference Name: IEEE Access}, - keywords = {adversarial training, attention mechanism, binary classification, interpretability, Knowledge discovery, natural language inference, Natural language processing, Perturbation methods, Predictive models, question answering, Robustness, Solid modeling, Task analysis, Training}, - pages = {92974--92985}, - file = {Full Text PDF:/home/alex/Zotero/storage/BUXFCXRU/Kitada and Iyatomi - 2021 - Attention Meets Perturbations Robust and Interpretable Attention With Adversarial Training.pdf:application/pdf;IEEE Xplore Abstract Record:/home/alex/Zotero/storage/ZNQD3FRW/9467291.html:text/html}, -} - -@inproceedings{choi_retain_2016, - title = {{RETAIN}: {An} {Interpretable} {Predictive} {Model} for {Healthcare} using {Reverse} {Time} {Attention} {Mechanism}}, - volume = {29}, - shorttitle = {{RETAIN}}, - url = {https://proceedings.neurips.cc/paper/2016/hash/231141b34c82aa95e48810a9d1b33a79-Abstract.html}, - abstract = {Accuracy and interpretability are two dominant features of successful predictive models. Typically, a choice must be made in favor of complex black box models such as recurrent neural networks (RNN) for accuracy versus less accurate but more interpretable traditional models such as logistic regression. This tradeoff poses challenges in medicine where both accuracy and interpretability are important. We addressed this challenge by developing the REverse Time AttentIoN model (RETAIN) for application to Electronic Health Records (EHR) data. RETAIN achieves high accuracy while remaining clinically interpretable and is based on a two-level neural attention model that detects influential past visits and significant clinical variables within those visits (e.g. key diagnoses). RETAIN mimics physician practice by attending the EHR data in a reverse time order so that recent clinical visits are likely to receive higher attention. RETAIN was tested on a large health system EHR dataset with 14 million visits completed by 263K patients over an 8 year period and demonstrated predictive accuracy and computational scalability comparable to state-of-the-art methods such as RNN, and ease of interpretability comparable to traditional models.}, - urldate = {2025-02-21}, - booktitle = {Advances in {Neural} {Information} {Processing} {Systems}}, - publisher = {Curran Associates, Inc.}, - author = {Choi, Edward and Bahadori, Mohammad Taha and Sun, Jimeng and Kulas, Joshua and Schuetz, Andy and Stewart, Walter}, - year = {2016}, - file = {Full Text PDF:/home/alex/Zotero/storage/XQLMYHUU/Choi et al. - 2016 - RETAIN An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism.pdf:application/pdf}, -} - -@misc{serrano_is_2019, - title = {Is {Attention} {Interpretable}?}, - url = {http://arxiv.org/abs/1906.03731}, - doi = {10.48550/arXiv.1906.03731}, - abstract = {Attention mechanisms have recently boosted performance on a range of NLP tasks. Because attention layers explicitly weight input components' representations, it is also often assumed that attention can be used to identify information that models found important (e.g., specific contextualized word tokens). We test whether that assumption holds by manipulating attention weights in already-trained text classification models and analyzing the resulting differences in their predictions. While we observe some ways in which higher attention weights correlate with greater impact on model predictions, we also find many ways in which this does not hold, i.e., where gradient-based rankings of attention weights better predict their effects than their magnitudes. We conclude that while attention noisily predicts input components' overall importance to a model, it is by no means a fail-safe indicator.}, - urldate = {2025-02-21}, - publisher = {arXiv}, - author = {Serrano, Sofia and Smith, Noah A.}, - month = jun, - year = {2019}, - note = {arXiv:1906.03731 [cs]}, - keywords = {Computer Science - Computation and Language}, - annote = {Comment: To appear at ACL 2019}, - file = {Preprint PDF:/home/alex/Zotero/storage/DFZ28RG8/Serrano and Smith - 2019 - Is Attention Interpretable.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/63B9XS2Y/1906.html:text/html}, -} - -@article{lyzwinski_innovative_2024, - title = {Innovative {Approaches} to {Menstruation} and {Fertility} {Tracking} {Using} {Wearable} {Reproductive} {Health} {Technology}: {Systematic} {Review}}, - volume = {26}, - shorttitle = {Innovative {Approaches} to {Menstruation} and {Fertility} {Tracking} {Using} {Wearable} {Reproductive} {Health} {Technology}}, - url = {https://www.jmir.org/2024/1/e45139}, - doi = {10.2196/45139}, - abstract = {Background: Emerging digital health technology has moved into the reproductive health market for female individuals. In the past, mobile health apps have been used to monitor the menstrual cycle using manual entry. New technological trends involve the use of wearable devices to track fertility by assessing physiological changes such as temperature, heart rate, and respiratory rate. -Objective: The primary aims of this study are to review the types of wearables that have been developed and evaluated for menstrual cycle tracking and to examine whether they may detect changes in the menstrual cycle in female individuals. Another aim is to review whether these devices are effective for tracking various stages in the menstrual cycle including ovulation and menstruation. Finally, the secondary aim is to assess whether the studies have validated their findings by reporting accuracy and sensitivity. -Methods: A review of PubMed or MEDLINE was undertaken to evaluate wearable devices for their effectiveness in predicting fertility and differentiating between the different stages of the menstrual cycle. -Results: Fertility cycle–tracking wearables include devices that can be worn on the wrists, on the fingers, intravaginally, and inside the ear. Wearable devices hold promise for predicting different stages of the menstrual cycle including the fertile window and may be used by female individuals as part of their reproductive health. Most devices had high accuracy for detecting fertility and were able to differentiate between the luteal phase (early and late), fertile window, and menstruation by assessing changes in heart rate, heart rate variability, temperature, and respiratory rate. -Conclusions: More research is needed to evaluate consumer perspectives on reproductive technology for monitoring fertility, and ethical issues around the privacy of digital data need to be addressed. Additionally, there is also a need for more studies to validate and confirm this research, given its scarcity, especially in relation to changes in respiratory rate as a proxy for reproductive cycle staging.}, - language = {EN}, - number = {1}, - urldate = {2025-02-21}, - journal = {Journal of Medical Internet Research}, - author = {Lyzwinski, Lynnette and Elgendi, Mohamed and Menon, Carlo}, - month = feb, - year = {2024}, - note = {Company: Journal of Medical Internet Research -Distributor: Journal of Medical Internet Research -Institution: Journal of Medical Internet Research -Label: Journal of Medical Internet Research -Publisher: JMIR Publications Inc., Toronto, Canada}, - pages = {e45139}, - file = {Full Text:/home/alex/Zotero/storage/IP23WZLE/Lyzwinski et al. - 2024 - Innovative Approaches to Menstruation and Fertility Tracking Using Wearable Reproductive Health Tech.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/TJN3WV3I/e45139.html:text/html}, -} - -@misc{noauthor_zyklus-apps_nodate, - title = {Zyklus-{Apps} zur {Verhütung} – sicher oder {Gesellschaftsspiel}? - {ProQuest}}, - shorttitle = {Zyklus-{Apps} zur {Verhütung} – sicher oder {Gesellschaftsspiel}?}, - url = {https://www.proquest.com/openview/739071fff0941b30f3a5d33b56259c60/1?pq-origsite=gscholar&cbl=6629261}, - abstract = {Explore millions of resources from scholarly journals, books, newspapers, videos and more, on the ProQuest Platform.}, - language = {en}, - urldate = {2025-02-21}, - file = {Snapshot:/home/alex/Zotero/storage/QHYUJU9G/1.html:text/html}, -} - -@article{goeckenjan_continuous_2020, - title = {Continuous {Body} {Temperature} {Monitoring} to {Improve} the {Diagnosis} of {Female} {Infertility}}, - volume = {80}, - copyright = {Georg Thieme Verlag KG Stuttgart · New York}, - issn = {0016-5751}, - url = {https://www.thieme-connect.com/products/ejournals/html/10.1055/a-1191-7888}, - doi = {10.1055/a-1191-7888}, - abstract = {Introduction Ovulatory dysfunction is a major cause of female infertility. We evaluated the use of continuous body temperature monitoring with a vaginal biosensor to improve - standard diagnostic procedures for determining ovulatory dysfunction. - -Material and Methods This prospective interventional study was performed in a reproductive medicine department of a university hospital. The menstrual cycles of 51 women with - infertility were monitored and analysed using three different strategies: sonographic and hormonal assessment (standard approach), continuous core body temperature measurement and analysis - using the algorithm of OvulaRing, and lowest daily body temperature measurement monitored with a vaginal biosensor and analysed based on the body temperature curves used in natural family - planning. - -Results Statistically significant differences were found in the temperature curves of women with luteal phase deficiency and polycystic ovary syndrome compared to women with normal - menstrual cycles. The analysis of individual cyclofertilograms can be used to detect cycle phases and estimate the date of ovulation. - -Conclusions Continuous body temperature monitoring with a vaginal biosensor can improve the standard diagnostic procedures used to determine ovulatory dysfunction, especially if - dysfunction is due to luteal phase deficiency and polycystic ovary syndrome. Analysis of the lowest daily body temperature combined with the basal body temperature measurements used in - fertility awareness methods may be equieffective to continuous body temperature measurements with OvulaRing. The results of this study show that a revised diagnostic approach using fewer - hormonal assessments combined with continuous body temperature monitoring can reduce the number of appointments in an infertility clinic as well as the costs.}, - language = {en}, - urldate = {2025-02-21}, - journal = {Geburtshilfe und Frauenheilkunde}, - author = {Goeckenjan, Maren and Schiwek, Esther and Wimberger, Pauline}, - month = jul, - year = {2020}, - note = {Publisher: Georg Thieme Verlag KG}, - keywords = {infertility, Key words - fertility awareness, luteal phase deficiency, polycystic ovary syndrome, vaginal biosensor}, - pages = {702--712}, - file = {Full Text PDF:/home/alex/Zotero/storage/QKPIJD23/Goeckenjan et al. - 2020 - Continuous Body Temperature Monitoring to Improve the Diagnosis of Female Infertility.pdf:application/pdf}, -} - -@article{regidor_identification_2018, - title = {Identification and prediction of the fertile window with a new web-based medical device using a vaginal biosensor for measuring the circadian and circamensual core body temperature}, - volume = {34}, - issn = {0951-3590}, - url = {https://doi.org/10.1080/09513590.2017.1390737}, - doi = {10.1080/09513590.2017.1390737}, - abstract = {Fertility awareness-based (FAB) methods represent a term that includes all family planning methods that are based on the identification of the fertile window. They are based on the woman’s observation of physiological signs of the fertile and infertile phases of the menstrual cycle. The first approach consists basically in symptothermal methods accompanied by cervical mucus measurements and clinical menstrual cycling data recording. The second most often used methods are the urinary measurement of E3G and luteinizing hormone (LH) with a personalized computer system. Hence these systems lack the efficacy of the continuous circadian and circamensual measurement of the core body temperature. Only this approach enables the accurate detection of the ovulation during the fertile window. A new medical device called OvulaRing has been developed to fill this gap. In the present study, the system and its first clinical results are presented. OvulaRing is a medical device used just like a tampon. The device is a vaginal ring of evatane that contains an integrated biosensor. This sensor measures continuously every 5 min the core body temperature throughout the entire cycle. This device allows a circadian and circamensual intravaginal exact measurement. With this system, 288 measurements are created per day. The system can detect retrospectively and predict prospectively the fertile window of the users. One hundred and fifty eight women aged between 18 and 45 years used this medical device in an open non-randomized clinical study for 15 months. A total of 470 cycles could be recorded and were able for analysis. By the same time in a subgroup of patients, hormonal assessments of LH, follicle-stimulating hormone, estradiol and progesterone as well as vaginal ultrasound were performed in parallel between the 9th and the 36th day of the cycle. The validation error due to software errors was 0.89\% for the retrospective analysis; that means that the accuracy for the detection of the ovulation was 99.11\%. Accuracy of 88.8\% for a window of 3 days before ovulation, the day of ovulation and the 3 days after ovulation was achieved for the prospective analysis. In the subgroup of woman with recorded pregnancies, it could be shown that after 3.79 months of use (median) pregnancies were observed. In 67.72\% in up to 3 months, in 16.36\% between 3 and 6 months of use, in 7.27\% between 7 and 9 months, in 5.45\% between 10 and 12 months and in 1.82\% between 13 and 15 months of use of the system. With this new web-based system, a precise determination of the fertile window even in women with ultralong cycles ({\textgreater}35 days) could be detected independently of their personal live circumstances. Exact determination of the fertile window is herewith possible so that OvulaRing represents an evolution in the FAB method for the cycle diagnosis of women with regular, irregular or anovulatory menstrual cycles.}, - number = {3}, - urldate = {2025-02-21}, - journal = {Gynecological Endocrinology}, - author = {Regidor, Pedro-Antonio and Kaczmarczyk, Marta and Schiweck, Esther and Goeckenjan-Festag, Maren and Alexander, Henry}, - month = mar, - year = {2018}, - pmid = {29082805}, - note = {Publisher: Taylor \& Francis -\_eprint: https://doi.org/10.1080/09513590.2017.1390737}, - keywords = {Infertility, central nervous system, circadian rhythm, circamensual rhythm, core body temperature, fertile window, vagina}, - pages = {256--260}, - file = {Full Text PDF:/home/alex/Zotero/storage/ITD68HTW/Regidor et al. - 2018 - Identification and prediction of the fertile window with a new web-based medical device using a vagi.pdf:application/pdf}, -} - -@article{alexander_fertilitatsmonitoring_2014, - title = {Fertilitätsmonitoring mit vaginalem {Biosensor} ({OvulaRing}©)}, - volume = {74}, - issn = {0016-5751}, - url = {https://www.thieme-connect.com/products/ejournals/abstract/10.1055/s-0034-1388603}, - doi = {10.1055/s-0034-1388603}, - abstract = {Thieme E-Books \& E-Journals}, - language = {de}, - urldate = {2025-02-21}, - journal = {Geburtshilfe und Frauenheilkunde}, - author = {Alexander, H. and Kaczmarczyk, M. and Pretzsch, G. and Kersken, T. and Puschmann, D. and Schiwek, E. and Goeckenjan, M.}, - month = sep, - year = {2014}, - keywords = {60. Kongress der Deutschen Gesellschaft für Gynäkologie und Geburtshilfe}, - pages = {FV\_08\_05}, - file = {Snapshot:/home/alex/Zotero/storage/HPL6XYJW/s-0034-1388603.html:text/html}, -} - -@inproceedings{regidor_identifizierung_2018, - title = {Identifizierung und {Vorhersage} des fertilen {Fensters} des weiblichen {Zyklus} mit einem neuen web basierten {Medizinprodukt} ({OvulaRing}®).}, - volume = {78}, - copyright = {Georg Thieme Verlag KG Stuttgart · New York}, - url = {https://www.thieme-connect.com/products/ejournals/html/10.1055/s-0038-1671278}, - doi = {10.1055/s-0038-1671278}, - abstract = {Thieme E-Books \& E-Journals}, - language = {de}, - urldate = {2025-02-21}, - booktitle = {Geburtshilfe und {Frauenheilkunde}}, - publisher = {Georg Thieme Verlag KG}, - author = {Regidor, P. A. and Alexander, H.}, - month = sep, - year = {2018}, - note = {ISSN: 0016-5751}, - keywords = {Präsidentin der DGGG e.V.: Prof. Dr. Birgit Seelbach-Göbel{\textless}/conf-president{\textgreater}{\textless}/conference{\textgreater}}, - pages = {P 23}, - file = {Snapshot:/home/alex/Zotero/storage/DW6578ZM/s-0038-1671278.html:text/html}, -} - -@article{sato_novel_2024, - title = {Novel {Methodology} for {Identifying} the {Occurrence} of {Ovulation} by {Estimating} {Core} {Body} {Temperature} {During} {Sleeping}: {Validity} and {Effectiveness} {Study}}, - volume = {8}, - copyright = {Unless stated otherwise, all articles are open-access distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work ("first published in the Journal of Medical Internet Research...") is properly cited with original URL and bibliographic citation information. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.}, - shorttitle = {Novel {Methodology} for {Identifying} the {Occurrence} of {Ovulation} by {Estimating} {Core} {Body} {Temperature} {During} {Sleeping}}, - url = {https://formative.jmir.org/2024/1/e55834}, - doi = {10.2196/55834}, - abstract = {Background: Body temperature is the most-used noninvasive biomarker to determine menstrual cycle and ovulation. However, issues related to its low accuracy are still under discussion. Objective: This study aimed to improve the accuracy of identifying the presence or absence of ovulation within a menstrual cycle. We investigated whether core body temperature (CBT) estimation can improve the accuracy of temperature biphasic shift discrimination in the menstrual cycle. The study consisted of 2 parts: experiment 1 assessed the validity of the CBT estimation method, while experiment 2 focused on the effectiveness of the method in discriminating biphasic temperature shifts. Methods: In experiment 1, healthy women aged between 18 and 40 years had their true CBT measured using an ingestible thermometer and their CBT estimated from skin temperature and ambient temperature measured during sleep in both the follicular and luteal phases of their menstrual cycles. This study analyzed the differences between these 2 measurements, the variations in temperature between the 2 phases, and the repeated measures correlation between the true and estimated CBT. Experiment 2 followed a similar methodology, but focused on evaluating the diagnostic accuracy of these 2 temperature measurement approaches (estimated CBT and traditional oral basal body temperature [BBT]) for identifying ovulatory cycles. This was performed using urine luteinizing hormone (LH) as the reference standard. Menstrual cycles were categorized based on the results of the LH tests, and a temperature shift was identified using a specific criterion called the “three-over-six rule.” This rule and the nested design of the study facilitated the assessment of diagnostic measures, such as sensitivity and specificity. Results: The main findings showed that CBT estimated from skin temperature and ambient temperature during sleep was consistently lower than directly measured CBT in both the follicular and luteal phases of the menstrual cycle. Despite this, the pattern of temperature variation between these phases was comparable for both the estimated and true CBT measurements, suggesting that the estimated CBT accurately reflected the cyclical variations in the true CBT. Significantly, the CBT estimation method showed higher sensitivity and specificity for detecting the occurrence of ovulation than traditional oral BBT measurements, highlighting its potential as an effective tool for reproductive health monitoring. The current method for estimating the CBT provides a practical and noninvasive method for monitoring CBT, which is essential for identifying biphasic shifts in the BBT throughout the menstrual cycle. Conclusions: This study demonstrated that the estimated CBT derived from skin temperature and ambient temperature during sleep accurately captures variations in true CBT and is more accurate in determining the presence or absence of ovulation than traditional oral BBT measurements. This method holds promise for improving reproductive health monitoring and understanding of menstrual cycle dynamics.}, - language = {EN}, - number = {1}, - urldate = {2025-02-20}, - journal = {JMIR Formative Research}, - author = {Sato, Daisuke and Ikarashi, Koyuki and Nakajima, Fumiko and Fujimoto, Tomomi}, - month = jul, - year = {2024}, - note = {Company: JMIR Formative Research -Distributor: JMIR Formative Research -Institution: JMIR Formative Research -Label: JMIR Formative Research -Publisher: JMIR Publications Inc., Toronto, Canada}, - pages = {e55834}, - file = {PubMed Central Full Text PDF:/home/alex/Zotero/storage/NPBA84BU/Sato et al. - 2024 - Novel Methodology for Identifying the Occurrence of Ovulation by Estimating Core Body Temperature Du.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/BAJUVPFE/e55834.html:text/html}, -} - -@article{royston_identifying_1991, - title = {Identifying the fertile phase of the human menstrual cycle}, +@article{b_s_novel_2022, + title = {Novel Technique for Confirmation of the Day of Ovulation and Prediction of Ovulation in Subsequent Cycles Using a Skin-Worn Sensor in a Population With Ovulatory Dysfunction: A Side-by-Side Comparison With Existing Basal Body Temperature Algorithm and Vaginal Core Body Temperature Algorithm}, volume = {10}, - copyright = {Copyright © 1991 John Wiley \& Sons, Ltd.}, - issn = {1097-0258}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.4780100207}, - doi = {10.1002/sim.4780100207}, - abstract = {The identification of the human fertile phase as the time during which a woman or a couple may conceive is elusive. The fertile time depends on many factors in each individual menstrual cycle and may be said to be more of a statistical than a physiological entity. This paper reviews the application of statistical methods to three areas related to conception and the fertile phase. The first is the prediction and detection of ovulation from serial measurements, such as hormones, basal body temperature and cervical mucus, throughout the menstrual cycle. Typically, such variables increase from some baseline level to a peak around ovulation (the most fertile time), then subside to low levels in the postovulatory phase. The statistical challenge is to detect the rise (signalling the onset of potential fertility) and subsequent fall. Analytic methods considered include thresholds, Bayesian change-point models and particularly the cumulative sum (cusum) technique which is both simple to apply and understand, and effective. The second area comprises appropriate methods of analysing and interpreting data from clinical studies of the fertile phase, especially in so-alled natural family planning (NFP) where it is usual for women to observe several indices of potential fertility. Such studies usually try to establish the temporal relationships between markers of the fertile phase and examine the success of different combinations of markers in delineating the fertile time in comparison with a standard ‘defined’ phase, for example, the interval from three days before to two days after the peak of luteinizing hormone. The third area is the assessment of the probability of conception on certain days of the cycle, which is vital to the understanding of the fertile phase and its application to NFP. Direct estimation of such probabilities is impractical; instead, resort must be made to estimation by maximum likelihood of the parameters of specially constructed models. Suitable models are described. Finally, the need for a new prospective study of the probability of conception in relation to the markers of the fertile phase used in the symptothermal method of NFP is discussed.}, - language = {en}, - number = {2}, - urldate = {2025-02-20}, - journal = {Statistics in Medicine}, - author = {Royston, Patrick}, - year = {1991}, - pages = {221--240}, - file = {Snapshot:/home/alex/Zotero/storage/9XL95LUJ/sim.html:text/html}, + issn = {2296-4185}, + url = {https://www.frontiersin.org/articles/10.3389/fbioe.2022.807139/full}, + doi = {10.3389/fbioe.2022.807139}, + shorttitle = {Novel Technique for Confirmation of the Day of Ovulation and Prediction of Ovulation in Subsequent Cycles Using a Skin-Worn Sensor in a Population With Ovulatory Dysfunction}, + abstract = {Objective: Determine the accuracy of a novel technique for confirmation of the day of ovulation and prediction of ovulation in subsequent cycles for the purpose of conception using a skin-worn sensor in a population with ovulatory dysfunction. +Methods: A total of 80 participants recorded consecutive overnight temperatures using a skin-worn sensor at the same time as a commercially available vaginal sensor for a total of 205 reproductive cycles. The vaginal sensor and its associated algorithm were used to determine the day of ovulation, and the ovulation results obtained using the skin-worn sensor and its associated algorithm were assessed for comparative accuracy alongside a number of other statistical techniques, with a further assessment of the same skin-derived data by means of the “three over six” rule. A number of parameters were used to divide the data into separate comparative groups, and further secondary statistical analyses were performed. +Results: The skin-worn sensor and its associated algorithm (together labeled “{SWS}”) were 66\% accurate for determining the day of ovulation (±1 day) or the absence of ovulation and 90\% accurate for determining the fertile window (ovulation day ±3 days) in the total study population in comparison to the results obtained from the vaginal sensor and its associated algorithm (together labeled “{VS}”). +Conclusion: {SWS} is a useful tool for confirming the fertile window and absence of ovulation (anovulation) in a population with ovulatory dysfunction, both known and Edited by:}, + pages = {807139}, + journaltitle = {Front. Bioeng. Biotechnol.}, + author = {B. S., Hurst and K., Davies and R. C., Milnes and T. G., Knowles and A., Pirrie}, + urldate = {2025-05-27}, + date = {2022-03-04}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/U8UPIT4Q/B. S. et al. - 2022 - Novel Technique for Confirmation of the Day of Ovulation and Prediction of Ovulation in Subsequent C.pdf:application/pdf}, } -@article{su_detection_2017, - title = {Detection of ovulation, a review of currently available methods}, - volume = {2}, - copyright = {© 2017 The Authors. Bioengineering \& Translational Medicine is published by Wiley Periodicals, Inc. on behalf of The American Institute of Chemical Engineers}, - issn = {2380-6761}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/btm2.10058}, - doi = {10.1002/btm2.10058}, - abstract = {The ability to identify the precise time of ovulation is important for women who want to plan conception or practice contraception. Here, we review the current literature on various methods for detecting ovulation including a review of point-of-care device technology. We incorporate an examination of methods to detect ovulation that have been developed and practiced for decades and analyze the indications and limitations of each—transvaginal ultrasonography, urinary luteinizing hormone detection, serum progesterone and urinary pregnanediol 3-glucuronide detection, urinary follicular stimulating hormone detection, basal body temperature monitoring, and cervical mucus and salivary ferning analysis. Some point-of-care ovulation detection devices have been developed and commercialized based on these methods, however previous research was limited by small sample size and an inconsistent standard reference to true ovulation.}, - language = {en}, - number = {3}, - urldate = {2025-02-20}, - journal = {Bioengineering \& Translational Medicine}, - author = {Su, Hsiu-Wei and Yi, Yu-Chiao and Wei, Ting-Yen and Chang, Ting-Chang and Cheng, Chao-Min}, - year = {2017}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/btm2.10058}, - keywords = {ovulation detection, family planning, fertility window}, - pages = {238--246}, - file = {Full Text PDF:/home/alex/Zotero/storage/ZEACCGE5/Su et al. - 2017 - Detection of ovulation, a review of currently available methods.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/RDDQD8EA/btm2.html:text/html}, -} - -@article{owen_physiological_2013, - title = {Physiological {Signs} of {Ovulation} and {Fertility} {Readily} {Observable} by {Women}}, - volume = {80}, - issn = {0024-3639}, - url = {https://doi.org/10.1179/0024363912Z.0000000005}, - doi = {10.1179/0024363912Z.0000000005}, - abstract = {IntroductionConfirmation of ovulation can be difficult in clinical practice, as gold standard methods including serial transvaginal ultrasonography, serum luteinizing hormone (LH) measurements, or laparoscopic follicular observation are impractical. Numerous surrogate markers have been proposed and evaluated in relation to these gold standards that have more practical clinical applications.PurposeTo review the evidence on physiological signs of ovulation timing and fertility in order to determine valid markers that can be easily identified by women.MethodsA literature review of primary resources in Ovid Medline was undertaken to identify studies examining physiological signs as they relate to gold standard assessment of ovulation. Studies examining the efficacy/effectiveness of different types of natural family planning were excluded.ResultsThe most commonly encountered physiological signs were urine LH, cervical mucus, and basal body temperature (BBT). Urine LH as assessed by home monitoring systems indicated ovulation 91 percent of the time during the 2 days of peak fertility on the monitor and 97 percent during the 2 peak days plus 1. Cervical mucus peak characteristics were identified 78 percent of the time ±1 day, and 91 percent of the time ±2 days of LH surge indicating ovulation. Further research supports the importance of cervical mucus in overall fertility, as conception rates were more closely related to mucus quality than to timing of intercourse related to ovulation. As a lone indicator of ovulation, BBT is at best a retrospective marker, and functions best in conjunction with other signs of ovulation. Additionally, salivary ferning, salivary and vaginal fluid electrical potential, finger–finger electrical potential, and differential skin temperature were postulated as possible indicators, but were not found to be temporally related to ovulation. The research on differential skin temperature is promising, but minimal thus far in number, and has not been evaluated as an adjunct to BBT as yet.ConclusionHome urinary LH monitors are becoming more widely available and less expensive giving women the potential to assess the ovulatory status of their cycle in real time. Cervical mucus observation is an effective and cost-efficient method, but requires some teaching to increase the confidence of users. In conjunction, LH monitors and cervical mucus can give the best indication of fertility and ovulation timing.}, - language = {en}, - number = {1}, - urldate = {2025-02-20}, - journal = {The Linacre Quarterly}, - author = {Owen, Martin}, - month = jan, - year = {2013}, - note = {Publisher: SAGE Publications Inc}, - pages = {17--23}, - file = {Full Text:/home/alex/Zotero/storage/IBVUICCU/Owen - 2013 - Physiological Signs of Ovulation and Fertility Readily Observable by Women.pdf:application/pdf}, -} - -@article{soumpasis_real-life_2020, - title = {Real-life insights on menstrual cycles and ovulation using big data}, - volume = {2020}, - issn = {2399-3529}, - url = {https://doi.org/10.1093/hropen/hoaa011}, - doi = {10.1093/hropen/hoaa011}, - abstract = {What variations underlie the menstrual cycle length and ovulation day of women trying to conceive?Big data from a connected ovulation test revealed the extent of variation in menstrual cycle length and ovulation day in women trying to conceive.Timing intercourse to coincide with the fertile period of a woman maximises the chances of conception. The day of ovulation varies on an inter- and intra-individual level.A total of 32 595 women who had purchased a connected ovulation test system contributed 75 981 cycles for analysis. Day of ovulation was determined from the fertility test results. The connected home ovulation test system enables users to identify their fertile phase. The app benefits users by enabling them to understand their personal fertility information. During each menstrual cycle, users input their perceived cycle length into an accessory application, and data on hormone levels from the tests are uploaded to the application and stored in an anonymised cloud database. This study compared users’ perceived cycle characteristics with actual cycle characteristics. The perceived and actual cycle length information was analysed to provide population ranges.This study analysed data from the at-home use of a commercially available connected home ovulation test by women across the USA and UK.Overall, 25.3\% of users selected a 28-day cycle as their perceived cycle length; however, only 12.4\% of users actually had a 28-day cycle. Most women (87\%) had actual menstrual cycle lengths between 23 and 35 days, with a normal distribution centred on day 28, and over half of the users (52\%) had cycles that varied by 5 days or more. There was a 10-day spread of observed ovulation days for a 28-day cycle, with the most common day of ovulation being Day 15. Similar variation was observed for all cycle lengths examined. For users who conducted a test on every day requested by the app, a luteinising hormone (LH) surge was detected in 97.9\% of cycles.Data were from a self-selected population of women who were prepared to purchase a commercially available product to aid conception and so may not fully represent the wider population. No corresponding demographic data were collected with the cycle information.Using big data has provided more personalised insights into women’s fertility; this could enable women trying to conceive to better time intercourse, increasing the likelihood of conception.The study was funded by SPD Development Company Ltd (Bedford, UK), a fully owned subsidiary of SPD Swiss Precision Diagnostics GmbH (Geneva, Switzerland). I.S., B.G. and S.J. are employees of the SPD Development Company Ltd.}, - number = {2}, - urldate = {2025-02-20}, - journal = {Human Reproduction Open}, - author = {Soumpasis, I and Grace, B and Johnson, S}, - month = feb, - year = {2020}, - pages = {hoaa011}, - annote = { - -not really interesting, as they only look at cycle length - - -they use “perceived” cycle length, which is hard to defend, when there are intermediate bleedings etc. - - - - - -}, - file = {Full Text PDF:/home/alex/Zotero/storage/PS9UC298/Soumpasis et al. - 2020 - Real-life insights on menstrual cycles and ovulation using big data.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/LTFATQIC/5820371.html:text/html}, -} - -@article{brewis_demographic_2005, - title = {Demographic {Evidence} {That} {Human} {Ovulation} {Is} {Undetectable} ({At} {Least} in {Pair} {Bonds})}, - volume = {46}, - issn = {0011-3204}, - url = {https://www.journals.uchicago.edu/doi/abs/10.1086/430016}, - doi = {10.1086/430016}, - number = {3}, - urldate = {2025-02-20}, - journal = {Current Anthropology}, - author = {Brewis, Alexandra and Meyer, Mary}, - month = jun, - year = {2005}, - note = {Publisher: The University of Chicago Press}, - pages = {465--471}, -} - -@article{noauthor_monitoring_1987, - title = {Monitoring techniques to predict and detect ovulation}, - volume = {47}, +@article{garcia_prediction_1981, + title = {Prediction of the Time of Ovulation*}, + volume = {36}, issn = {0015-0282}, - url = {https://www.sciencedirect.com/science/article/pii/S0015028216500028}, - doi = {10.1016/S0015-0282(16)50002-8}, - abstract = {This study was designed to evaluate the accuracy of various methods in predicting and detecting ovulation in 14 spontaneous and 17 clomiphene citrate …}, - language = {en-US}, - number = {2}, - urldate = {2025-02-20}, - journal = {Fertility and Sterility}, - month = feb, - year = {1987}, - note = {Publisher: Elsevier}, - pages = {259--264}, - file = {PDF:/home/alex/Zotero/storage/RLCZVH5V/1987 - Monitoring techniques to predict and detect ovulation.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/M5P9EZ67/S0015028216500028.html:text/html}, + url = {https://www.sciencedirect.com/science/article/pii/S0015028216457304}, + doi = {10.1016/S0015-0282(16)45730-4}, + abstract = {Prediction of ovulation was established by correlation of clinical parameters, follicular development by ultrasound, and estradiol, progesterone, and luteinizing hormone ({LH}) determination in 71 menstrual cycles. Laparoscopic follicular aspiration was accomplished in 41 of those cycles. A 28-hour interval from the ascending limb of the {LH} seems to be the “ideal time” for retrieval of a preovulatory oocyte. The variability in the amount of {LH} to which the follicle is exposed during the {LH} surge seems to indicate that there is a relatively low specific value necessary for ovulation. Ovulation occurs approximately 10 ± 5 hours from the {LH} peak. Progesterone occurs in relation to the {LH} surge and is helpful for the retrospective analysis of the menstrual cycle.}, + pages = {308--315}, + number = {3}, + journaltitle = {Fertility and Sterility}, + author = {Garcia, Jairo E. and Seegar Jones, Georgeanna and Wright, George L.}, + urldate = {2025-05-27}, + date = {1981-09-01}, + file = {PDF:/home/alex/Zotero/storage/DB8PW3QR/Garcia et al. - 1981 - Prediction of the Time of Ovulation.pdf:application/pdf;ScienceDirect Snapshot:/home/alex/Zotero/storage/QLFLZFDJ/S0015028216457304.html:text/html}, } -@article{noauthor_physiological_2016, - title = {Physiological predictors of ovulation and pregnancy risk in a fixed-time artificial insemination program}, - volume = {99}, - issn = {0022-0302}, - url = {https://www.sciencedirect.com/science/article/pii/S0022030216306725}, - doi = {10.3168/jds.2016-11247}, - abstract = {The objective of this study was to determine the relative importance and contribution of several physiological factors as predictors of pregnancy risk…}, - language = {en-US}, - number = {12}, - urldate = {2025-02-20}, - journal = {Journal of Dairy Science}, - month = dec, - year = {2016}, - note = {Publisher: Elsevier}, - pages = {10077--10092}, - file = {Snapshot:/home/alex/Zotero/storage/EBK9WJP4/S0022030216306725.html:text/html}, +@misc{wang_timexer_2024, + title = {{TimeXer}: Empowering Transformers for Time Series Forecasting with Exogenous Variables}, + url = {http://arxiv.org/abs/2402.19072}, + doi = {10.48550/arXiv.2402.19072}, + shorttitle = {{TimeXer}}, + abstract = {Deep models have demonstrated remarkable performance in time series forecasting. However, due to the partially-observed nature of real-world applications, solely focusing on the target of interest, so-called endogenous variables, is usually insufficient to guarantee accurate forecasting. Notably, a system is often recorded into multiple variables, where the exogenous variables can provide valuable external information for endogenous variables. Thus, unlike well-established multivariate or univariate forecasting paradigms that either treat all the variables equally or ignore exogenous information, this paper focuses on a more practical setting: time series forecasting with exogenous variables. We propose a novel approach, {TimeXer}, to ingest external information to enhance the forecasting of endogenous variables. With deftly designed embedding layers, {TimeXer} empowers the canonical Transformer with the ability to reconcile endogenous and exogenous information, where patch-wise self-attention and variate-wise cross-attention are used simultaneously. Moreover, global endogenous tokens are learned to effectively bridge the causal information underlying exogenous series into endogenous temporal patches. Experimentally, {TimeXer} achieves consistent state-of-the-art performance on twelve real-world forecasting benchmarks and exhibits notable generality and scalability. Code is available at this repository: https://github.com/thuml/{TimeXer}.}, + number = {{arXiv}:2402.19072}, + publisher = {{arXiv}}, + author = {Wang, Yuxuan and Wu, Haixu and Dong, Jiaxiang and Qin, Guo and Zhang, Haoran and Liu, Yong and Qiu, Yunzhong and Wang, Jianmin and Long, Mingsheng}, + urldate = {2025-05-09}, + date = {2024-11-11}, + eprinttype = {arxiv}, + eprint = {2402.19072 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, + file = {Full Text PDF:/home/alex/Zotero/storage/76BQWVIW/Wang et al. - 2024 - TimeXer Empowering Transformers for Time Series Forecasting with Exogenous Variables.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/JL64E9YL/2402.html:text/html}, } -@inproceedings{serrano_is_2019-1, - address = {Florence, Italy}, - title = {Is {Attention} {Interpretable}?}, - url = {https://aclanthology.org/P19-1282/}, - doi = {10.18653/v1/P19-1282}, - abstract = {Attention mechanisms have recently boosted performance on a range of NLP tasks. Because attention layers explicitly weight input components' representations, it is also often assumed that attention can be used to identify information that models found important (e.g., specific contextualized word tokens). We test whether that assumption holds by manipulating attention weights in already-trained text classification models and analyzing the resulting differences in their predictions. While we observe some ways in which higher attention weights correlate with greater impact on model predictions, we also find many ways in which this does not hold, i.e., where gradient-based rankings of attention weights better predict their effects than their magnitudes. We conclude that while attention noisily predicts input components' overall importance to a model, it is by no means a fail-safe indicator.}, - urldate = {2025-02-19}, - booktitle = {Proceedings of the 57th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics}}, - publisher = {Association for Computational Linguistics}, - author = {Serrano, Sofia and Smith, Noah A.}, - editor = {Korhonen, Anna and Traum, David and Màrquez, Lluís}, - month = jul, - year = {2019}, - pages = {2931--2951}, - file = {Full Text PDF:/home/alex/Zotero/storage/I6J6YP3C/Serrano and Smith - 2019 - Is Attention Interpretable.pdf:application/pdf}, +@misc{zeng_are_2022, + title = {Are Transformers Effective for Time Series Forecasting?}, + url = {http://arxiv.org/abs/2205.13504}, + doi = {10.48550/arXiv.2205.13504}, + abstract = {Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting ({LTSF}) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the elements in a long sequence. However, in time series modeling, we are to extract the temporal relations in an ordered set of continuous points. While employing positional encoding and using tokens to embed sub-series in Transformers facilitate preserving some ordering information, the nature of the {\textbackslash}emph\{permutation-invariant\} self-attention mechanism inevitably results in temporal information loss. To validate our claim, we introduce a set of embarrassingly simple one-layer linear models named {LTSF}-Linear for comparison. Experimental results on nine real-life datasets show that {LTSF}-Linear surprisingly outperforms existing sophisticated Transformer-based {LTSF} models in all cases, and often by a large margin. Moreover, we conduct comprehensive empirical studies to explore the impacts of various design elements of {LTSF} models on their temporal relation extraction capability. We hope this surprising finding opens up new research directions for the {LTSF} task. We also advocate revisiting the validity of Transformer-based solutions for other time series analysis tasks (e.g., anomaly detection) in the future. Code is available at: {\textbackslash}url\{https://github.com/cure-lab/{LTSF}-Linear\}.}, + number = {{arXiv}:2205.13504}, + publisher = {{arXiv}}, + author = {Zeng, Ailing and Chen, Muxi and Zhang, Lei and Xu, Qiang}, + urldate = {2025-05-09}, + date = {2022-08-17}, + eprinttype = {arxiv}, + eprint = {2205.13504 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, + file = {Full Text PDF:/home/alex/Zotero/storage/V9E95F7E/Zeng et al. - 2022 - Are Transformers Effective for Time Series Forecasting.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/HNVIZG98/2205.html:text/html}, } -@article{noauthor_attention-based_2023, - title = {An attention-based deep learning model for multi-horizon time series forecasting by considering periodic characteristic}, - volume = {185}, - issn = {0360-8352}, - url = {https://www.sciencedirect.com/science/article/abs/pii/S0360835223006915}, - doi = {10.1016/j.cie.2023.109667}, - abstract = {Recently, transformer-based models have exhibited great performance in multi-horizon time series forecasting tasks. However, the core module of these …}, - language = {en-US}, - urldate = {2025-02-19}, - journal = {Computers \& Industrial Engineering}, - month = nov, - year = {2023}, - note = {Publisher: Pergamon}, - pages = {109667}, - file = {Snapshot:/home/alex/Zotero/storage/TJ634T5V/S0360835223006915.html:text/html}, +@article{barrera-animas_rainfall_2022, + title = {Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting}, + volume = {7}, + issn = {26668270}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S266682702100102X}, + doi = {10.1016/j.mlwa.2021.100204}, + shorttitle = {Rainfall prediction}, + abstract = {Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statistical models that are often too costly, both computationally and budgetary, or are not applied to downstream applications. Therefore, approaches that use Machine Learning algorithms in conjunction with time-series data are being explored as an alternative to overcome these drawbacks. To this end, this study presents a comparative analysis using simplified rainfall estimation models based on conventional Machine Learning algorithms and Deep Learning architectures that are efficient for these downstream applications. Models based on {LSTM}, Stacked-{LSTM}, Bidirectional-{LSTM} Networks, {XGBoost}, and an ensemble of Gradient Boosting Regressor, Linear Support Vector Regression, and an Extra-trees Regressor were compared in the task of forecasting hourly rainfall volumes using time-series data. Climate data from 2000 to 2020 from five major cities in the United Kingdom were used. The evaluation metrics of Loss, Root Mean Squared Error, Mean Absolute Error, and Root Mean Squared Logarithmic Error were used to evaluate the models’ performance. Results show that a Bidirectional-{LSTM} Network can be used as a rainfall forecast model with comparable performance to Stacked-{LSTM} Networks. Among all the models tested, the {StackedLSTM} Network with two hidden layers and the Bidirectional-{LSTM} Network performed best. This suggests that models based on {LSTM}-Networks with fewer hidden layers perform better for this approach; denoting its ability to be applied as an approach for budget-wise rainfall forecast applications.}, + pages = {100204}, + journaltitle = {Machine Learning with Applications}, + author = {Barrera-Animas, Ari Yair and Oyedele, Lukumon O. and Bilal, Muhammad and Akinosho, Taofeek Dolapo and Delgado, Juan Manuel Davila and Akanbi, Lukman Adewale}, + urldate = {2025-05-08}, + date = {2022-03}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/FSXMH5N9/Barrera-Animas et al. - 2022 - Rainfall prediction A comparative analysis of modern machine learning algorithms for time-series fo.pdf:application/pdf}, } -@article{hu_pattern-oriented_2025, - title = {Pattern-oriented {Attention} {Mechanism} for {Multivariate} {Time} {Series} {Forecasting}}, - volume = {19}, - issn = {1556-4681}, - url = {https://doi.org/10.1145/3712606}, - doi = {10.1145/3712606}, - abstract = {Multivariate time series forecasting is applied in many domains, such as finance, transportation, and industry. The main challenge of precise forecasting lies in accurately capturing latent dependencies. Recent studies develop various frameworks to reduce computational complexity or to enhance the learning of intricate relationships, while lacking interpretability and generality. In this article, we aim to elucidate the capture of dependencies as the recognition of patterns. We believe that patterns can be formally described from two aspects: the shapes of segments that frequently repeat and the corresponding forms of repetitions. Drawing upon this idea, we design a multivariate time series forecasting model named PRformer,1 which incorporates a pattern-oriented attention mechanism and a pattern-based projector. The attention mechanism can perceive different forms of repetitions by embedded with various similarity evaluation metrics between segments, and filter out noise from segments to extract potential patterns with a statistical-driven weighting scheme. The pattern-based projector is employed to form the forecasting results by deriving the representative patterns from the set of potential ones. By incorporating explicit definitions of patterns, PRformer is interpretable and general to various time series scenarios. Experimental results on seven datasets demonstrate that PRformer outperforms six state-of-the-art models by about 10.7\% in forecasting accuracy.}, - number = {2}, - urldate = {2025-02-19}, - journal = {ACM Trans. Knowl. Discov. Data}, - author = {Hu, Hanwen and Han, Zhangchi and Qian, Shiyou and Yang, Dingyu and Cao, Jian and Xue, Guangtao}, - month = feb, - year = {2025}, - pages = {38:1--38:26}, +@article{ahmed_empirical_2010, + title = {An Empirical Comparison of Machine Learning Models for Time Series Forecasting}, + volume = {29}, + issn = {0747-4938, 1532-4168}, + url = {http://www.tandfonline.com/doi/abs/10.1080/07474938.2010.481556}, + doi = {10.1080/07474938.2010.481556}, + pages = {594--621}, + number = {5}, + journaltitle = {Econometric Reviews}, + author = {Ahmed, Nesreen K. and Atiya, Amir F. and Gayar, Neamat El and El-Shishiny, Hisham}, + urldate = {2025-05-08}, + date = {2010-08-30}, + langid = {english}, } -@misc{helbling_conceptattention_2025, - title = {{ConceptAttention}: {Diffusion} {Transformers} {Learn} {Highly} {Interpretable} {Features}}, - shorttitle = {{ConceptAttention}}, - url = {http://arxiv.org/abs/2502.04320}, - doi = {10.48550/arXiv.2502.04320}, - abstract = {Do the rich representations of multi-modal diffusion transformers (DiTs) exhibit unique properties that enhance their interpretability? We introduce ConceptAttention, a novel method that leverages the expressive power of DiT attention layers to generate high-quality saliency maps that precisely locate textual concepts within images. Without requiring additional training, ConceptAttention repurposes the parameters of DiT attention layers to produce highly contextualized concept embeddings, contributing the major discovery that performing linear projections in the output space of DiT attention layers yields significantly sharper saliency maps compared to commonly used cross-attention mechanisms. Remarkably, ConceptAttention even achieves state-of-the-art performance on zero-shot image segmentation benchmarks, outperforming 11 other zero-shot interpretability methods on the ImageNet-Segmentation dataset and on a single-class subset of PascalVOC. Our work contributes the first evidence that the representations of multi-modal DiT models like Flux are highly transferable to vision tasks like segmentation, even outperforming multi-modal foundation models like CLIP.}, - urldate = {2025-02-25}, - publisher = {arXiv}, - author = {Helbling, Alec and Meral, Tuna Han Salih and Hoover, Ben and Yanardag, Pinar and Chau, Duen Horng}, - month = feb, - year = {2025}, - note = {arXiv:2502.04320 [cs]}, - keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition}, - file = {Preprint PDF:/home/alex/Zotero/storage/AEEDM4ZW/Helbling et al. - 2025 - ConceptAttention Diffusion Transformers Learn Highly Interpretable Features.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/SAHGAINH/2502.html:text/html}, +@misc{saluja_towards_2021, + title = {Towards a Rigorous Evaluation of Explainability for Multivariate Time Series}, + url = {http://arxiv.org/abs/2104.04075}, + doi = {10.48550/arXiv.2104.04075}, + abstract = {Machine learning-based systems are rapidly gaining popularity and in-line with that there has been a huge research surge in the field of explainability to ensure that machine learning models are reliable, fair, and can be held liable for their decision-making process. Explainable Artificial Intelligence ({XAI}) methods are typically deployed to debug black-box machine learning models but in comparison to tabular, text, and image data, explainability in time series is still relatively unexplored. The aim of this study was to achieve and evaluate model agnostic explainability in a time series forecasting problem. This work focused on proving a solution for a digital consultancy company aiming to find a data-driven approach in order to understand the effect of their sales related activities on the sales deals closed. The solution involved framing the problem as a time series forecasting problem to predict the sales deals and the explainability was achieved using two novel model agnostic explainability techniques, Local explainable model-agnostic explanations ({LIME}) and Shapley additive explanations ({SHAP}) which were evaluated using human evaluation of explainability. The results clearly indicate that the explanations produced by {LIME} and {SHAP} greatly helped lay humans in understanding the predictions made by the machine learning model. The presented work can easily be extended to any time}, + number = {{arXiv}:2104.04075}, + publisher = {{arXiv}}, + author = {Saluja, Rohit and Malhi, Avleen and Knapič, Samanta and Främling, Kary and Cavdar, Cicek}, + urldate = {2025-05-06}, + date = {2021-04-06}, + eprinttype = {arxiv}, + eprint = {2104.04075 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, + file = {Preprint PDF:/home/alex/Zotero/storage/6XB73Y2F/Saluja et al. - 2021 - Towards a Rigorous Evaluation of Explainability for Multivariate Time Series.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/CDCGX8EZ/2104.html:text/html}, } -@misc{chefer_transformer_2021, - title = {Transformer {Interpretability} {Beyond} {Attention} {Visualization}}, - url = {http://arxiv.org/abs/2012.09838}, - doi = {10.48550/arXiv.2012.09838}, - abstract = {Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks. In order to visualize the parts of the image that led to a certain classification, existing methods either rely on the obtained attention maps or employ heuristic propagation along the attention graph. In this work, we propose a novel way to compute relevancy for Transformer networks. The method assigns local relevance based on the Deep Taylor Decomposition principle and then propagates these relevancy scores through the layers. This propagation involves attention layers and skip connections, which challenge existing methods. Our solution is based on a specific formulation that is shown to maintain the total relevancy across layers. We benchmark our method on very recent visual Transformer networks, as well as on a text classification problem, and demonstrate a clear advantage over the existing explainability methods.}, - urldate = {2025-02-25}, - publisher = {arXiv}, - author = {Chefer, Hila and Gur, Shir and Wolf, Lior}, - month = apr, - year = {2021}, - note = {arXiv:2012.09838 [cs]}, - keywords = {Computer Science - Computer Vision and Pattern Recognition}, - file = {Preprint PDF:/home/alex/Zotero/storage/3FRISAP7/Chefer et al. - 2021 - Transformer Interpretability Beyond Attention Visualization.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/QUPBJPC9/2012.html:text/html}, +@inproceedings{hsieh_explainable_2021, + location = {Virtual Event Israel}, + title = {Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns to Attend to Important Variables As Well As Time Intervals}, + isbn = {978-1-4503-8297-7}, + url = {https://dl.acm.org/doi/10.1145/3437963.3441815}, + doi = {10.1145/3437963.3441815}, + shorttitle = {Explainable Multivariate Time Series Classification}, + eventtitle = {{WSDM} '21: The Fourteenth {ACM} International Conference on Web Search and Data Mining}, + pages = {607--615}, + booktitle = {Proceedings of the 14th {ACM} International Conference on Web Search and Data Mining}, + publisher = {{ACM}}, + author = {Hsieh, Tsung-Yu and Wang, Suhang and Sun, Yiwei and Honavar, Vasant}, + urldate = {2025-05-06}, + date = {2021-03-08}, + langid = {english}, } -@misc{sprang_enforcing_2024, - title = {Enforcing {Interpretability} in {Time} {Series} {Transformers}: {A} {Concept} {Bottleneck} {Framework}}, - shorttitle = {Enforcing {Interpretability} in {Time} {Series} {Transformers}}, - url = {http://arxiv.org/abs/2410.06070}, - doi = {10.48550/arXiv.2410.06070}, - abstract = {There has been a recent push of research on Transformer-based models for long-term time series forecasting, even though they are inherently difficult to interpret and explain. While there is a large body of work on interpretability methods for various domains and architectures, the interpretability of Transformer-based forecasting models remains largely unexplored. To address this gap, we develop a framework based on Concept Bottleneck Models to enforce interpretability of time series Transformers. We modify the training objective to encourage a model to develop representations similar to predefined interpretable concepts. In our experiments, we enforce similarity using Centered Kernel Alignment, and the predefined concepts include time features and an interpretable, autoregressive surrogate model (AR). We apply the framework to the Autoformer model, and present an in-depth analysis for a variety of benchmark tasks. We find that the model performance remains mostly unaffected, while the model shows much improved interpretability. Additionally, interpretable concepts become local, which makes the trained model easily intervenable. As a proof of concept, we demonstrate a successful intervention in the scenario of a time shift in the data, which eliminates the need to retrain.}, - urldate = {2025-02-25}, - publisher = {arXiv}, - author = {Sprang, Angela van and Acar, Erman and Zuidema, Willem}, - month = oct, - year = {2024}, - note = {arXiv:2410.06070 [cs]}, +@article{leon-lopez_anomaly_2022, + title = {Anomaly Detection and Classification in Multispectral Time Series Based on Hidden Markov Models}, + volume = {60}, + issn = {1558-0644}, + url = {https://ieeexplore.ieee.org/abstract/document/9509347}, + doi = {10.1109/TGRS.2021.3101127}, + abstract = {Monitoring agriculture from satellite remote sensing data, such as multispectral images, has become a powerful tool since it has demonstrated a great potential for providing timely and accurate knowledge of crops. Detecting anomalies in time series of multispectral remote sensing images for crop monitoring is generally performed using a large sample of historical data at a pixel level. Conversely, this article presents a framework for anomaly detection ({AD}), localization, and classification that exploits the temporal information contained in a given season at a parcel level to detect and localize outliers using hidden Markov models ({HMMs}). Specifically, the {AD} part is based on the learning of {HMM} parameters associated with unlabeled normal data that are used in a second step to detect abnormal crop parcels referred to as anomalies. The learned {HMM} can also be used in time segments to temporally localize the anomalies affecting the crop parcels. The detected and localized anomalies are finally classified using a supervised classifier, e.g., based on support vector machines. The proposed framework is applicable to images partially covered by clouds and can handle a set of crop parcels acquired in the same season bypassing problems due to crop rotations. Numerical experiments are conducted on synthetic and real data, where the real data correspond to vegetation indices extracted from several multitemporal Sentinel-2 images of rapeseed crops. The proposed approach is compared to standard {AD} methods yielding better detection rates with the advantage of allowing anomalies to be localized and characterized.}, + pages = {1--11}, + journaltitle = {{IEEE} Transactions on Geoscience and Remote Sensing}, + author = {León-López, Kareth M. and Mouret, Florian and Arguello, Henry and Tourneret, Jean-Yves}, + urldate = {2025-05-06}, + date = {2022}, + keywords = {Time series analysis, Agricultural monitoring, Agriculture, anomaly classification, Anomaly detection, anomaly detection ({AD}), Feature extraction, Hidden Markov models, hidden Markov models ({HMMs}), Monitoring, remote sensing, time series, Vegetation mapping}, + file = {Snapshot:/home/alex/Zotero/storage/HIWT8MDH/9509347.html:text/html;Submitted Version:/home/alex/Zotero/storage/S4K2XXHN/León-López et al. - 2022 - Anomaly Detection and Classification in Multispectral Time Series Based on Hidden Markov Models.pdf:application/pdf}, +} + +@article{wang_systematic_2022, + title = {A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications}, + volume = {22}, + rights = {https://creativecommons.org/licenses/by/4.0/}, + issn = {1424-8220}, + url = {https://www.mdpi.com/1424-8220/22/20/8016}, + doi = {10.3390/s22208016}, + abstract = {Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification ({TSC}) is very commonly used for modeling digital clinical measures. While deep learning models for {TSC} are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on {PubMed}, as well as the Institute of Electrical and Electronics Engineers ({IEEE}), Web of Science, and {SCOPUS} databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: {PubMed}, {IEEE}, Web of Science Database, and {SCOPUS}. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful.}, + pages = {8016}, + number = {20}, + journaltitle = {Sensors}, + author = {Wang, Will Ke and Chen, Ina and Hershkovich, Leeor and Yang, Jiamu and Shetty, Ayush and Singh, Geetika and Jiang, Yihang and Kotla, Aditya and Shang, Jason Zisheng and Yerrabelli, Rushil and Roghanizad, Ali R. and Shandhi, Md Mobashir Hasan and Dunn, Jessilyn}, + urldate = {2025-05-06}, + date = {2022-10-20}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/H5LLUB5K/Wang et al. - 2022 - A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications.pdf:application/pdf}, +} + +@article{masini_machine_2023, + title = {Machine learning advances for time series forecasting}, + volume = {37}, + issn = {0950-0804, 1467-6419}, + url = {https://onlinelibrary.wiley.com/doi/10.1111/joes.12429}, + doi = {10.1111/joes.12429}, + abstract = {Abstract + In this paper, we survey the most recent advances in supervised machine learning ({ML}) and high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods, we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feedforward and recurrent versions, and tree‐based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of {ML} in economics and finance and provide an illustration with high‐frequency financial data.}, + pages = {76--111}, + number = {1}, + journaltitle = {Journal of Economic Surveys}, + author = {Masini, Ricardo P. and Medeiros, Marcelo C. and Mendes, Eduardo F.}, + urldate = {2025-05-06}, + date = {2023-02}, + langid = {english}, + file = {Submitted Version:/home/alex/Zotero/storage/4TZJJUSU/Masini et al. - 2023 - Machine learning advances for time series forecasting.pdf:application/pdf}, +} + +@article{gharehbaghi_deep_2018, + title = {A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network}, + volume = {29}, + rights = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/{IEEE}.html}, + issn = {2162-237X, 2162-2388}, + url = {https://ieeexplore.ieee.org/document/8066455/}, + doi = {10.1109/TNNLS.2017.2754294}, + pages = {4102--4115}, + number = {9}, + journaltitle = {{IEEE} Trans. Neural Netw. Learning Syst.}, + author = {Gharehbaghi, Arash and Linden, Maria}, + urldate = {2025-05-06}, + date = {2018-09}, +} + +@article{wang_systematic_2022-1, + title = {A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications}, + volume = {22}, + issn = {1424-8220}, + url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9611376/}, + doi = {10.3390/s22208016}, + abstract = {Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification ({TSC}) is very commonly used for modeling digital clinical measures. While deep learning models for {TSC} are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on {PubMed}, as well as the Institute of Electrical and Electronics Engineers ({IEEE}), Web of Science, and {SCOPUS} databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: {PubMed}, {IEEE}, Web of Science Database, and {SCOPUS}. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful.}, + pages = {8016}, + number = {20}, + journaltitle = {Sensors (Basel)}, + author = {Wang, Will Ke and Chen, Ina and Hershkovich, Leeor and Yang, Jiamu and Shetty, Ayush and Singh, Geetika and Jiang, Yihang and Kotla, Aditya and Shang, Jason Zisheng and Yerrabelli, Rushil and Roghanizad, Ali R. and Shandhi, Md Mobashir Hasan and Dunn, Jessilyn}, + urldate = {2025-05-06}, + date = {2022-10-20}, + pmid = {36298367}, + pmcid = {PMC9611376}, + file = {Full Text PDF:/home/alex/Zotero/storage/2E8EIVER/Wang et al. - 2022 - A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications.pdf:application/pdf}, +} + +@misc{dauphin_language_2017, + title = {Language Modeling with Gated Convolutional Networks}, + url = {http://arxiv.org/abs/1612.08083}, + doi = {10.48550/arXiv.1612.08083}, + abstract = {The pre-dominant approach to language modeling to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded context. In this paper we develop a finite context approach through stacked convolutions, which can be more efficient since they allow parallelization over sequential tokens. We propose a novel simplified gating mechanism that outperforms Oord et al (2016) and investigate the impact of key architectural decisions. The proposed approach achieves state-of-the-art on the {WikiText}-103 benchmark, even though it features long-term dependencies, as well as competitive results on the Google Billion Words benchmark. Our model reduces the latency to score a sentence by an order of magnitude compared to a recurrent baseline. To our knowledge, this is the first time a non-recurrent approach is competitive with strong recurrent models on these large scale language tasks.}, + number = {{arXiv}:1612.08083}, + publisher = {{arXiv}}, + author = {Dauphin, Yann N. and Fan, Angela and Auli, Michael and Grangier, David}, + urldate = {2025-03-26}, + date = {2017-09-08}, + eprinttype = {arxiv}, + eprint = {1612.08083 [cs]}, + keywords = {Computer Science - Computation and Language}, + file = {Full Text PDF:/home/alex/Zotero/storage/4SBUNZ4A/Dauphin et al. - 2017 - Language Modeling with Gated Convolutional Networks.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/TQBL4EZ7/1612.html:text/html}, +} + +@misc{ba_layer_2016, + title = {Layer Normalization}, + url = {http://arxiv.org/abs/1607.06450}, + doi = {10.48550/arXiv.1607.06450}, + abstract = {Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute a mean and variance which are then used to normalize the summed input to that neuron on each training case. This significantly reduces the training time in feed-forward neural networks. However, the effect of batch normalization is dependent on the mini-batch size and it is not obvious how to apply it to recurrent neural networks. In this paper, we transpose batch normalization into layer normalization by computing the mean and variance used for normalization from all of the summed inputs to the neurons in a layer on a single training case. Like batch normalization, we also give each neuron its own adaptive bias and gain which are applied after the normalization but before the non-linearity. Unlike batch normalization, layer normalization performs exactly the same computation at training and test times. It is also straightforward to apply to recurrent neural networks by computing the normalization statistics separately at each time step. Layer normalization is very effective at stabilizing the hidden state dynamics in recurrent networks. Empirically, we show that layer normalization can substantially reduce the training time compared with previously published techniques.}, + number = {{arXiv}:1607.06450}, + publisher = {{arXiv}}, + author = {Ba, Jimmy Lei and Kiros, Jamie Ryan and Hinton, Geoffrey E.}, + urldate = {2025-03-26}, + date = {2016-07-21}, + eprinttype = {arxiv}, + eprint = {1607.06450 [stat]}, + keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, + file = {Full Text PDF:/home/alex/Zotero/storage/MJWRDPWE/Ba et al. - 2016 - Layer Normalization.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/F9WSU957/1607.html:text/html}, +} + +@misc{clevert_fast_2016, + title = {Fast and Accurate Deep Network Learning by Exponential Linear Units ({ELUs})}, + url = {http://arxiv.org/abs/1511.07289}, + doi = {10.48550/arXiv.1511.07289}, + abstract = {We introduce the "exponential linear unit" ({ELU}) which speeds up learning in deep neural networks and leads to higher classification accuracies. Like rectified linear units ({ReLUs}), leaky {ReLUs} ({LReLUs}) and parametrized {ReLUs} ({PReLUs}), {ELUs} alleviate the vanishing gradient problem via the identity for positive values. However, {ELUs} have improved learning characteristics compared to the units with other activation functions. In contrast to {ReLUs}, {ELUs} have negative values which allows them to push mean unit activations closer to zero like batch normalization but with lower computational complexity. Mean shifts toward zero speed up learning by bringing the normal gradient closer to the unit natural gradient because of a reduced bias shift effect. While {LReLUs} and {PReLUs} have negative values, too, they do not ensure a noise-robust deactivation state. {ELUs} saturate to a negative value with smaller inputs and thereby decrease the forward propagated variation and information. Therefore, {ELUs} code the degree of presence of particular phenomena in the input, while they do not quantitatively model the degree of their absence. In experiments, {ELUs} lead not only to faster learning, but also to significantly better generalization performance than {ReLUs} and {LReLUs} on networks with more than 5 layers. On {CIFAR}-100 {ELUs} networks significantly outperform {ReLU} networks with batch normalization while batch normalization does not improve {ELU} networks. {ELU} networks are among the top 10 reported {CIFAR}-10 results and yield the best published result on {CIFAR}-100, without resorting to multi-view evaluation or model averaging. On {ImageNet}, {ELU} networks considerably speed up learning compared to a {ReLU} network with the same architecture, obtaining less than 10\% classification error for a single crop, single model network.}, + number = {{arXiv}:1511.07289}, + publisher = {{arXiv}}, + author = {Clevert, Djork-Arné and Unterthiner, Thomas and Hochreiter, Sepp}, + urldate = {2025-03-26}, + date = {2016-02-22}, + eprinttype = {arxiv}, + eprint = {1511.07289 [cs]}, keywords = {Computer Science - Machine Learning}, - file = {Preprint PDF:/home/alex/Zotero/storage/HVUXXRXJ/Sprang et al. - 2024 - Enforcing Interpretability in Time Series Transformers A Concept Bottleneck Framework.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/6QS78DZP/2410.html:text/html}, + file = {Full Text PDF:/home/alex/Zotero/storage/PU3ZGP4G/Clevert et al. - 2016 - Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/3MAW2IWE/1511.html:text/html}, } -@article{yuan_dcfa-itimenet_2024, - title = {{DCFA}-{iTimeNet}: {Dynamic} cross-fusion attention network for interpretable time series prediction}, - volume = {55}, - issn = {1573-7497}, - shorttitle = {{DCFA}-{iTimeNet}}, - url = {https://doi.org/10.1007/s10489-024-05973-2}, - doi = {10.1007/s10489-024-05973-2}, - abstract = {Although time series prediction research among engineering and technology has made breakthrough progress in performance, challenges remain in modeling complex dynamic interactions between variables and interpretability. To address these two problems, a novel two-stage strategy framework called DCFA-iTimeNet is introduced. In the first stage, this paper innovatively proposes a dynamic cross-fusion attention mechanism (DCFA) . This module facilitates the model to exchange information between different patches of the time series, thereby capturing the complex interactions between variables across time. In the second stage, we exploit a decomposition-based linear explainable Bidirectional Gated Recurrent Unit (DeLEBiGRU), which consists mainly of standard BiGRU and tensorized BiGRU. It is proposed to analyze each variable’s historical long-term, instantaneous, and future impacts. Such design is crucial for understanding how each variable impacts the overall prediction over time. Extensive experimental results demonstrate that the proposed model can effectively model and interpret complex dynamic relationships of multivariate time series and understand the model’s decision-making process. Moreover, the performance outperforms the state-of-the-art methods.}, - language = {en}, - number = {2}, - urldate = {2025-02-25}, - journal = {Applied Intelligence}, - author = {Yuan, Jianjun and Wu, Fujun and Zhao, Luoming and Pan, Dongbo and Yu, Xinyue}, - month = dec, - year = {2024}, - keywords = {Artificial Intelligence, Dynamic cross-fusion attention, Dynamic interaction, Interpretability, Time series prediction}, - pages = {86}, - file = {Full Text PDF:/home/alex/Zotero/storage/JEXYN7BN/Yuan et al. - 2024 - DCFA-iTimeNet Dynamic cross-fusion attention network for interpretable time series prediction.pdf:application/pdf}, +@online{noauthor_temporal_nodate, + title = {Temporal Fusion Transformer ({TFT}) — darts documentation}, + url = {https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html}, + urldate = {2025-03-19}, + file = {Temporal Fusion Transformer (TFT) — darts documentation:/home/alex/Zotero/storage/5QNI6WSL/darts.models.forecasting.tft_model.html:text/html}, } -@inproceedings{guo_exploring_2019, - title = {Exploring interpretable {LSTM} neural networks over multi-variable data}, - url = {https://proceedings.mlr.press/v97/guo19b.html}, - abstract = {For recurrent neural networks trained on time series with target and exogenous variables, in addition to accurate prediction, it is also desired to provide interpretable insights into the data. In this paper, we explore the structure of LSTM recurrent neural networks to learn variable-wise hidden states, with the aim to capture different dynamics in multi-variable time series and distinguish the contribution of variables to the prediction. With these variable-wise hidden states, a mixture attention mechanism is proposed to model the generative process of the target. Then we develop associated training methods to jointly learn network parameters, variable and temporal importance w.r.t the prediction of the target variable. Extensive experiments on real datasets demonstrate enhanced prediction performance by capturing the dynamics of different variables. Meanwhile, we evaluate the interpretation results both qualitatively and quantitatively. It exhibits the prospect as an end-to-end framework for both forecasting and knowledge extraction over multi-variable data.}, - language = {en}, - urldate = {2025-02-25}, - booktitle = {Proceedings of the 36th {International} {Conference} on {Machine} {Learning}}, - publisher = {PMLR}, - author = {Guo, Tian and Lin, Tao and Antulov-Fantulin, Nino}, - month = may, - year = {2019}, - note = {ISSN: 2640-3498}, - pages = {2494--2504}, - file = {Full Text PDF:/home/alex/Zotero/storage/VV3I2T4E/Guo et al. - 2019 - Exploring interpretable LSTM neural networks over multi-variable data.pdf:application/pdf;Supplementary PDF:/home/alex/Zotero/storage/57IK29PA/Guo et al. - 2019 - Exploring interpretable LSTM neural networks over multi-variable data.pdf:application/pdf}, +@software{sherar_mattsherartemporal_fusion_transform_2025, + title = {mattsherar/Temporal\_Fusion\_Transform}, + url = {https://github.com/mattsherar/Temporal_Fusion_Transform}, + abstract = {Pytorch Implementation of Google's {TFT}}, + author = {Sherar, Matthew}, + urldate = {2025-03-19}, + date = {2025-03-07}, + note = {original-date: 2020-01-11T17:54:01Z}, } -@incollection{iliadis_temporal_2023, - address = {Cham}, - title = {Temporal {Attention} {Signatures} for {Interpretable} {Time}-{Series} {Prediction}}, - volume = {14259}, - isbn = {978-3-031-44222-3 978-3-031-44223-0}, - url = {https://link.springer.com/10.1007/978-3-031-44223-0_22}, - abstract = {Deep neural networks have become a staple in time-series prediction due to their remarkable accuracy. However, their internal workings often remain elusive. Significant advancements have been made in the interpretability of these networks, with attention mechanisms and feature maps being notably effective for image classification by highlighting the crucial data points. While human observers can readily confirm the significance of features in image classification, the interpretability of time-series data and its modeling remains challenging. To address this, we put forth an innovative approach that unifies temporal attention and visualization as a blend of recurrent neural networks, self-attention, and general attention. This synergy results in the generation of temporal attention signatures, akin to image attention heat maps. Temporal attention not only enhances prediction accuracy beyond that of recurrent networks alone but also demonstrates that varying label classes yield distinct attention signatures. This observation indicates that neural networks focus on different sections of time-series sequences contingent on the prediction target. We conclude with a discussion on the practical implications of this novel approach, including its applicability to model interpretation, sequence length selection, and model validation. This leads to more accurate, robust, and interpretable models, instilling greater confidence in their results.}, - language = {en}, - urldate = {2025-02-25}, - booktitle = {Artificial {Neural} {Networks} and {Machine} {Learning} – {ICANN} 2023}, - publisher = {Springer Nature Switzerland}, - author = {Katrompas, Alexander and Metsis, Vangelis}, - editor = {Iliadis, Lazaros and Papaleonidas, Antonios and Angelov, Plamen and Jayne, Chrisina}, - year = {2023}, - doi = {10.1007/978-3-031-44223-0_22}, - note = {Series Title: Lecture Notes in Computer Science}, - pages = {268--280}, - file = {PDF:/home/alex/Zotero/storage/LV7IVKZK/Katrompas and Metsis - 2023 - Temporal Attention Signatures for Interpretable Time-Series Prediction.pdf:application/pdf}, +@software{noauthor_playtikaosstft-torch_2025, + title = {{PlaytikaOSS}/tft-torch}, + rights = {{MIT}}, + url = {https://github.com/PlaytikaOSS/tft-torch}, + abstract = {A Python library that implements ״Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting״}, + publisher = {Playtika}, + urldate = {2025-03-19}, + date = {2025-01-27}, + note = {original-date: 2021-11-28T07:08:32Z}, } -@inproceedings{schwenke_constructing_2021, - title = {Constructing {Global} {Coherence} {Representations}: {Identifying} {Interpretability} and {Coherences} of {Transformer} {Attention} in {Time} {Series} {Data}}, - shorttitle = {Constructing {Global} {Coherence} {Representations}}, - url = {https://ieeexplore.ieee.org/document/9564126/?arnumber=9564126}, - doi = {10.1109/DSAA53316.2021.9564126}, - abstract = {Transformer models have shown significant advances recently based on the general concept of Attention — to focus on specifically important and relevant parts of the input data. However, methods for enhancing their interpretability and explainability are still lacking. This is the problem which we tackle in this paper, to make Multi-Headed Attention more interpretable and explainable for time series classification. We present a method for constructing global coherence representations from Multi-Headed Attention of Transformer architectures. Accordingly, we present abstraction and interpretation methods, leading to intuitive visualizations of the respective attention patterns. We evaluate our proposed approach and the presented methods on several datasets demonstrating their efficacy.}, - urldate = {2025-02-25}, - booktitle = {2021 {IEEE} 8th {International} {Conference} on {Data} {Science} and {Advanced} {Analytics} ({DSAA})}, - author = {Schwenke, Leonid and Atzmueller, Martin}, - month = oct, - year = {2021}, - keywords = {Interpretability, Attention, Coherence, Comprehensibility, Conferences, Data science, Data visualization, Deep Learning, Explainability, Global Class Representation, Scalability, Time series analysis, Time Series Classification, Transformer, Transformers, Visualization}, - pages = {1--12}, - file = {Full Text PDF:/home/alex/Zotero/storage/VRGIDFY8/Schwenke and Atzmueller - 2021 - Constructing Global Coherence Representations Identifying Interpretability and Coherences of Transf.pdf:application/pdf;IEEE Xplore Abstract Record:/home/alex/Zotero/storage/Z45R3XWF/9564126.html:text/html}, +@online{noauthor_create_nodate, + title = {Create baseline model - {ValueError}: too many values to unpack (expected 2) · Issue \#230 · sktime/pytorch-forecasting}, + url = {https://github.com/sktime/pytorch-forecasting/issues/230}, + shorttitle = {Create baseline model - {ValueError}}, + abstract = {{PyTorch}-Forecasting version: 0.7.1 {PyTorch} version: 1.7.1 Python version: 3.7 Operating System: {MAC} {OS} Big Sur: Version 11.1 Expected behavior I executed code actuals = torch.cat([y for x, (y, weig...}, + titleaddon = {{GitHub}}, + urldate = {2025-03-19}, + langid = {english}, } -@article{schwenke_show_nodate, - title = {Show {Me} {What} {You}’re {Looking} {For}: {Visualizing} {Abstracted} {Transformer} {Attention} for {Enhancing} {Their} {Local} {Interpretability} on {Time} {Series} {Data}}, - abstract = {While Transformers have shown their advantages considering their learning performance, their lack of explainability and interpretability is still a major problem. This specifically relates to the processing of time series, as a specific form of complex data. In this paper, we propose an approach for visualizing abstracted information in order to enable computational sensemaking and local interpretability on the respective Transformer model. Our results demonstrate the efficacy of the proposed abstraction method and visualization, utilizing both synthetic and real world data for evaluation.}, - language = {en}, - author = {Schwenke, Leonid and Atzmueller, Martin}, - file = {PDF:/home/alex/Zotero/storage/SLVVAAXA/Schwenke and Atzmueller - Show Me What You’re Looking For Visualizing Abstracted Transformer Attention for Enhancing Their Lo.pdf:application/pdf}, +@collection{pfannstiel_entrepreneurship_2018, + location = {Wiesbaden}, + title = {Entrepreneurship im Gesundheitswesen {II}}, + rights = {http://www.springer.com/tdm}, + isbn = {978-3-658-14780-8 978-3-658-14781-5}, + url = {http://link.springer.com/10.1007/978-3-658-14781-5}, + publisher = {Springer Fachmedien Wiesbaden}, + editor = {Pfannstiel, Mario A. and Da-Cruz, Patrick and Rasche, Christoph}, + urldate = {2025-03-17}, + date = {2018}, + langid = {german}, + doi = {10.1007/978-3-658-14781-5}, + file = {PDF:/home/alex/Zotero/storage/DTVM5NBP/Pfannstiel et al. - 2018 - Entrepreneurship im Gesundheitswesen II.pdf:application/pdf}, } -@article{wu_interpretable_2022, - title = {Interpretable wind speed prediction with multivariate time series and temporal fusion transformers}, - volume = {252}, - issn = {03605442}, - url = {https://linkinghub.elsevier.com/retrieve/pii/S0360544222008933}, - doi = {10.1016/j.energy.2022.123990}, - abstract = {Wind power has been utilized well in power systems, so steady and successful wind speed forecasting is crucial to security management power grid market economy. To date, most researchers have often discounted the interpretability of prediction models, leading to obscure forecasts. This study puts forward a unique forecasting methodology that incorporates notable decomposition techniques, multifactor interpretable forecasting models, and optimization algorithms. In the proposed model, variational mode decomposition is employed to break down the raw wind speed sequence into a set of intrinsic mode functions. Adaptive differential evolution is then used for optimizing several parameters of temporal fusion transformers (TFT) to achieve satisfactory forecasting performance. TFT is a new attention-based deep learning model that puts together high-performance multi-horizon prediction and interpretable insights into temporal dynamics. Empirical studies using eight real-world 1-h wind speed data sets in Albert, Canada, and Five Points, USA demonstrate that the system using the proposed model outperforms those employing other comparable models in nearly all performance metrics. Examples of TFT's interpretable outputs are the importance ranking of the decomposed wind speed sub-sequences and meteorological data and attention analysis of different step lengths. The findings signify substantial progress for wind speed prediction and aid policymakers.}, - language = {en}, - urldate = {2025-02-25}, - journal = {Energy}, - author = {Wu, Binrong and Wang, Lin and Zeng, Yu-Rong}, - month = aug, - year = {2022}, - pages = {123990}, - file = {PDF:/home/alex/Zotero/storage/MHAFY926/Wu et al. - 2022 - Interpretable wind speed prediction with multivariate time series and temporal fusion transformers.pdf:application/pdf}, +@article{murray_diagnosis_2005, + title = {Diagnosis and treatment of ectopic pregnancy}, + volume = {173}, + issn = {0820-3946, 1488-2329}, + url = {http://www.cmaj.ca/cgi/doi/10.1503/cmaj.050222}, + doi = {10.1503/cmaj.050222}, + abstract = {{ECTOPIC} {PREGNANCY} {IS} A {LIFE}- {AND} {FERTILITY}-threatening condition that is commonly seen in Canadian emergency departments. Increases in the availability and use of hormonal markers, coupled with advances in formal and emergency ultrasonography have changed the diagnostic approach to the patient in the emergency department with first-trimester bleeding or pain. Ultrasonography should be the initial investigation for symptomatic women in their first trimester; when the results are indeterminate, the serum β human chorionic gonadotropin (β-{hCG}) concentration should be measured. Serial measurement of β-{hCG} and progesterone concentrations may be useful when the diagnosis remains unclear. Advances in surgical and medical therapy for ectopic pregnancy have allowed the proliferation of minimally invasive or noninvasive treatment. Guidelines for laparoscopy and for methotrexate therapy are provided.}, + pages = {905--912}, + number = {8}, + journaltitle = {Canadian Medical Association Journal}, + author = {Murray, H.}, + urldate = {2025-03-17}, + date = {2005-10-11}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/Y73KE57K/Murray - 2005 - Diagnosis and treatment of ectopic pregnancy.pdf:application/pdf}, } -@inproceedings{habib_n-beats_2025, - address = {Cham}, - title = {N-{BEATS} \& {Temporal} {Fusion} {Transformer} {Based} {Surface} {Temperature} {Prediction} and {Forecasting} for {Realizing} {Global} {Warming} {Trends}}, - isbn = {978-3-031-75167-7}, - doi = {10.1007/978-3-031-75167-7_3}, - abstract = {At the pinnacle of civilization, where the impacts of climate change have been increasingly felt, weather prediction plays a critical role in mitigating the potential disasters that may arise. Moreover, with the gradual change on climate, surface temperature of the earth is increasing. This increasing rate of the surface temperature causing global warming which is a matter of intimidation. To leave off this global warming, weather forecasting can be used as an arsenal. Selecting the appropriate tools and models for weather prediction is a crucial step in ensuring accurate forecasts. In this research paper, the focus was on studying the versatility of three specific architectures for weather prediction: LSTM, Temporal Fusion Transformer, and N-BEATS. To assess these architectures’ performance, we conducted a number of experiments. With the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of the three, NBEATS stood out. This shows that when compared to the other models, the N-BEATS architecture had greater prediction accuracy. It's vital to remember, too, that the trials also showed that the Temporal Fusion Transformer and LSTM performed well. The only distinction was that these models required larger sizes in terms of parameters and computational complexity to achieve their performance levels. Consequently, considering both performance and model size, the researchers determined that N-BEATS was the most optimal and versatile architecture for weather prediction. Its ability to achieve excellent results with a smaller model size makes it a favorable choice for practical applications.}, - language = {en}, - booktitle = {Artificial {Intelligence} and {Speech} {Technology}}, - publisher = {Springer Nature Switzerland}, - author = {Habib, Adria Binte and Ashraf, Faisal Bin and Hossain, Muhammad Iqbal and Alam, Golam Rabiul}, - editor = {Dev, Amita and Sharma, Arun and Agrawal, S. S. and Rani, Ritu}, - year = {2025}, - keywords = {LSTM, N-BEATS, Temporal Fusion Transformer, Time Series Analysis, Weather Prediction}, - pages = {30--41}, - annote = { - -shows that n beats has better performance on smaller datasets - - -we have a relatively large dataset, thus tft will be focused on - - -}, +@article{rosenfield_adolescent_2013, + title = {Adolescent Anovulation: Maturational Mechanisms and Implications}, + volume = {98}, + issn = {0021-972X, 1945-7197}, + url = {https://academic.oup.com/jcem/article-lookup/doi/10.1210/jc.2013-1770}, + doi = {10.1210/jc.2013-1770}, + shorttitle = {Adolescent Anovulation}, + pages = {3572--3583}, + number = {9}, + journaltitle = {The Journal of Clinical Endocrinology \& Metabolism}, + author = {Rosenfield, Robert L.}, + urldate = {2025-03-17}, + date = {2013-09}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/BEF7PQT6/Rosenfield - 2013 - Adolescent Anovulation Maturational Mechanisms and Implications.pdf:application/pdf}, } -@article{papacharalampous_predictability_2018, - title = {Predictability of monthly temperature and precipitation using automatic time series forecasting methods}, - volume = {66}, - issn = {1895-7455}, - url = {https://doi.org/10.1007/s11600-018-0120-7}, - doi = {10.1007/s11600-018-0120-7}, - abstract = {We investigate the predictability of monthly temperature and precipitation by applying automatic univariate time series forecasting methods to a sample of 985 40-year-long monthly temperature and 1552 40-year-long monthly precipitation time series. The methods include a naïve one based on the monthly values of the last year, as well as the random walk (with drift), AutoRegressive Fractionally Integrated Moving Average (ARFIMA), exponential smoothing state-space model with Box–Cox transformation, ARMA errors, Trend and Seasonal components (BATS), simple exponential smoothing, Theta and Prophet methods. Prophet is a recently introduced model inspired by the nature of time series forecasted at Facebook and has not been applied to hydrometeorological time series before, while the use of random walk, BATS, simple exponential smoothing and Theta is rare in hydrology. The methods are tested in performing multi-step ahead forecasts for the last 48 months of the data. We further investigate how different choices of handling the seasonality and non-normality affect the performance of the models. The results indicate that: (a) all the examined methods apart from the naïve and random walk ones are accurate enough to be used in long-term applications; (b) monthly temperature and precipitation can be forecasted to a level of accuracy which can barely be improved using other methods; (c) the externally applied classical seasonal decomposition results mostly in better forecasts compared to the automatic seasonal decomposition used by the BATS and Prophet methods; and (d) Prophet is competitive, especially when it is combined with externally applied classical seasonal decomposition.}, - language = {en}, - number = {4}, - urldate = {2025-03-04}, - journal = {Acta Geophysica}, - author = {Papacharalampous, Georgia and Tyralis, Hristos and Koutsoyiannis, Demetris}, - month = aug, - year = {2018}, - keywords = {ARFIMA, Multi-step ahead forecasting, Precipitation forecasting, Prophet, Temperature forecasting, Time series forecasting}, - pages = {807--831}, +@book{cryer_time_2008, + location = {New York}, + edition = {2nd ed}, + title = {Time series analysis: with applications in R}, + isbn = {978-0-387-75958-6 978-0-387-75959-3}, + series = {Springer texts in statistics}, + shorttitle = {Time series analysis}, + pagetotal = {491}, + publisher = {Springer}, + author = {Cryer, Jonathan D. and Chan, Kung-sik}, + date = {2008}, + langid = {english}, + note = {{OCLC}: ocn191760003}, + keywords = {Data processing, R (Computer program language), Time-series analysis}, + file = {PDF:/home/alex/Zotero/storage/CIYMBUEW/Cryer and Chan - 2008 - Time series analysis with applications in R.pdf:application/pdf}, } -@article{dunson_day-specific_1999, - title = {Day-specific probabilities of clinical pregnancy based on two studies with imperfect measures of ovulation}, - volume = {14}, - issn = {1460-2350, 0268-1161}, - url = {https://academic.oup.com/humrep/article-lookup/doi/10.1093/humrep/14.7.1835}, - doi = {10.1093/humrep/14.7.1835}, - language = {en}, - number = {7}, - urldate = {2025-03-05}, - journal = {Human Reproduction}, - author = {Dunson, D.B. and Baird, D.D. and Wilcox, A.J. and Weinberg, C.R.}, - month = jul, - year = {1999}, - pages = {1835--1839}, - file = {PDF:/home/alex/Zotero/storage/8DE3ZLPJ/Dunson et al. - 1999 - Day-specific probabilities of clinical pregnancy based on two studies with imperfect measures of ovu.pdf:application/pdf}, +@book{hamilton_time_1994, + location = {Princeton (N.J.)}, + title = {Time series analysis}, + isbn = {978-0-691-04289-3}, + publisher = {Princeton university press}, + author = {Hamilton, James Douglas}, + date = {1994}, + file = {PDF:/home/alex/Zotero/storage/J8GVKKHG/Hamilton - 1994 - Time series analysis.pdf:application/pdf}, } -@misc{wikimedia_commons_basic_2019, - title = {Basic {Female} {Reproductive} {System}}, +@artwork{wikimedia_commons_basic_2019, + title = {Basic Female Reproductive System}, url = {https://en.wikipedia.org/wiki/File:Basic_Female_Reproductive_System_(English).svg}, author = {Wikimedia Commons}, - year = {2019}, + date = {2019}, file = {background_female_reproductive_organs:/home/alex/Zotero/storage/5MAJ4CG5/background_female_reproductive_organs.png:image/png}, } @article{silberstein_physiology_2000, - title = {Physiology of the {Menstrual} {Cycle}}, + title = {Physiology of the Menstrual Cycle}, volume = {20}, - copyright = {https://journals.sagepub.com/page/policies/text-and-data-mining-license}, + rights = {https://journals.sagepub.com/page/policies/text-and-data-mining-license}, issn = {0333-1024, 1468-2982}, url = {https://journals.sagepub.com/doi/10.1046/j.1468-2982.2000.00034.x}, doi = {10.1046/j.1468-2982.2000.00034.x}, - abstract = {The normal female life cycle is associated with a number of hormonal milestones: menarche, pregnancy, contraceptive use, menopause, and the use of replacement sex hormones. All these events and interventions alter the levels and cycling of sex hormones and may cause a change in the prevalence or intensity of headache. The menstrual cycle is the result of a carefully orchestrated sequence of interactions among the hypothalamus, pituitary, ovary, and endometrium, with the sex hormones acting as modulators and effectors at each level. Oestrogen and progestins have potent effects on central serotonergic and opioid neurons, modulating both neuronal activity and receptor density. The primary trigger of menstrual migraine appears to be the withdrawal of oestrogen rather than the maintenance of sustained high or low oestrogen levels. However, changes in the sustained oestrogen levels with pregnancy (increased) and menopause (decreased) appear to affect headaches. Headaches that occur with premenstrual syndrome appear to be centrally generated, involving the inherent rhythm of CNS neurons, including perhaps the serotonergic pain-modulating systems.}, - language = {en}, - number = {3}, - urldate = {2025-03-10}, - journal = {Cephalalgia}, - author = {Silberstein, S D and Merriam, G R}, - month = apr, - year = {2000}, + abstract = {The normal female life cycle is associated with a number of hormonal milestones: menarche, pregnancy, contraceptive use, menopause, and the use of replacement sex hormones. All these events and interventions alter the levels and cycling of sex hormones and may cause a change in the prevalence or intensity of headache. The menstrual cycle is the result of a carefully orchestrated sequence of interactions among the hypothalamus, pituitary, ovary, and endometrium, with the sex hormones acting as modulators and effectors at each level. Oestrogen and progestins have potent effects on central serotonergic and opioid neurons, modulating both neuronal activity and receptor density. The primary trigger of menstrual migraine appears to be the withdrawal of oestrogen rather than the maintenance of sustained high or low oestrogen levels. However, changes in the sustained oestrogen levels with pregnancy (increased) and menopause (decreased) appear to affect headaches. Headaches that occur with premenstrual syndrome appear to be centrally generated, involving the inherent rhythm of {CNS} neurons, including perhaps the serotonergic pain-modulating systems.}, pages = {148--154}, + number = {3}, + journaltitle = {Cephalalgia}, + author = {Silberstein, S D and Merriam, G R}, + urldate = {2025-03-10}, + date = {2000-04}, + langid = {english}, file = {Full Text:/home/alex/Zotero/storage/EL585H2P/Silberstein and Merriam - 2000 - Physiology of the Menstrual Cycle.pdf:application/pdf}, } -@misc{pedroso_menstrual_2022, - title = {The {Menstrual} {Cycle}}, +@online{pedroso_menstrual_2022, + title = {The Menstrual Cycle}, url = {https://kindbody.com/the-menstrual-cycle/}, abstract = {Fertility, gynecology, and wellness services in modern, tech-enabled clinics. Best-in-class care, accessible pricing, and a seamless patient experience.}, - urldate = {2025-03-10}, - journal = {Kindbody}, + titleaddon = {Kindbody}, author = {Pedroso, Dr Jasmine}, - month = jun, - year = {2022}, + urldate = {2025-03-10}, + date = {2022-06-03}, file = {Snapshot:/home/alex/Zotero/storage/738JEKW7/the-menstrual-cycle.html:text/html}, } @article{munster_length_1992, - title = {Length and variation in the menstrual cycle—a cross‐sectional study from a {Danish} county}, + title = {Length and variation in the menstrual cycle—a cross‐sectional study from a Danish county}, volume = {99}, issn = {1470-0328, 1471-0528}, url = {https://obgyn.onlinelibrary.wiley.com/doi/10.1111/j.1471-0528.1992.tb13762.x}, doi = {10.1111/j.1471-0528.1992.tb13762.x}, - abstract = {ABSTRACT + abstract = {{ABSTRACT} Objective To investigate the current epidemiology of menstrual patters among women of fertile age. @@ -1241,50 +1007,60 @@ we have a relatively large dataset, thus tft will be focused on Conclusion The study confirmed the normally used definitions of polymenorrhoea (cycle length {\textless}21 days) and oligomenorrhoea (cycle length between 36 and 90 days), as these very short or long menstrual cycle lengths were very seldom recorded for a longer period. However, the high frequency in a normal population of large menstrual cycle length variation challenges the view that an intra‐individual variation of {\textgreater}5 days should be regarded as a sign of disease in the woman.}, - language = {en}, - number = {5}, - urldate = {2025-03-07}, - journal = {BJOG: An International Journal of Obstetrics \& Gynaecology}, - author = {Münster, Kirstine and Schmidt, Lone and Helm, Peter}, - month = may, - year = {1992}, pages = {422--429}, + number = {5}, + journaltitle = {{BJOG}}, + author = {Münster, Kirstine and Schmidt, Lone and Helm, Peter}, + urldate = {2025-03-07}, + date = {1992-05}, + langid = {english}, file = {PDF:/home/alex/Zotero/storage/JWE75VL3/Münster et al. - 1992 - Length and variation in the menstrual cycle—a cross‐sectional study from a Danish county.pdf:application/pdf}, } @article{bull_real-world_2019, title = {Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles}, volume = {2}, - copyright = {2019 The Author(s)}, + rights = {2019 The Author(s)}, issn = {2398-6352}, url = {https://www.nature.com/articles/s41746-019-0152-7}, doi = {10.1038/s41746-019-0152-7}, - abstract = {The use of apps that record detailed menstrual cycle data presents a new opportunity to study the menstrual cycle. The aim of this study is to describe menstrual cycle characteristics observed from a large database of cycles collected through an app and investigate associations of menstrual cycle characteristics with cycle length, age and body mass index (BMI). Menstrual cycle parameters, including menstruation, basal body temperature (BBT) and luteinising hormone (LH) tests as well as age and BMI were collected anonymously from real-world users of the Natural Cycles app. We analysed 612,613 ovulatory cycles with a mean length of 29.3 days from 124,648 users. The mean follicular phase length was 16.9 days (95\% CI: 10–30) and mean luteal phase length was 12.4 days (95\% CI: 7–17). Mean cycle length decreased by 0.18 days (95\% CI: 0.17–0.18, R2 = 0.99) and mean follicular phase length decreased by 0.19 days (95\% CI: 0.19–0.20, R2 = 0.99) per year of age from 25 to 45 years. Mean variation of cycle length per woman was 0.4 days or 14\% higher in women with a BMI of over 35 relative to women with a BMI of 18.5–25. This analysis details variations in menstrual cycle characteristics that are not widely known yet have significant implications for health and well-being. Clinically, women who wish to plan a pregnancy need to have intercourse on their fertile days. In order to identify the fertile period it is important to track physiological parameters such as basal body temperature and not just cycle length.}, - language = {en}, + abstract = {The use of apps that record detailed menstrual cycle data presents a new opportunity to study the menstrual cycle. The aim of this study is to describe menstrual cycle characteristics observed from a large database of cycles collected through an app and investigate associations of menstrual cycle characteristics with cycle length, age and body mass index ({BMI}). Menstrual cycle parameters, including menstruation, basal body temperature ({BBT}) and luteinising hormone ({LH}) tests as well as age and {BMI} were collected anonymously from real-world users of the Natural Cycles app. We analysed 612,613 ovulatory cycles with a mean length of 29.3 days from 124,648 users. The mean follicular phase length was 16.9 days (95\% {CI}: 10–30) and mean luteal phase length was 12.4 days (95\% {CI}: 7–17). Mean cycle length decreased by 0.18 days (95\% {CI}: 0.17–0.18, R2 = 0.99) and mean follicular phase length decreased by 0.19 days (95\% {CI}: 0.19–0.20, R2 = 0.99) per year of age from 25 to 45 years. Mean variation of cycle length per woman was 0.4 days or 14\% higher in women with a {BMI} of over 35 relative to women with a {BMI} of 18.5–25. This analysis details variations in menstrual cycle characteristics that are not widely known yet have significant implications for health and well-being. Clinically, women who wish to plan a pregnancy need to have intercourse on their fertile days. In order to identify the fertile period it is important to track physiological parameters such as basal body temperature and not just cycle length.}, + pages = {1--8}, number = {1}, - urldate = {2025-03-07}, - journal = {npj Digital Medicine}, + journaltitle = {npj Digit. Med.}, author = {Bull, Jonathan R. and Rowland, Simon P. and Scherwitzl, Elina Berglund and Scherwitzl, Raoul and Danielsson, Kristina Gemzell and Harper, Joyce}, - month = aug, - year = {2019}, + urldate = {2025-03-07}, + date = {2019-08-27}, + langid = {english}, note = {Publisher: Nature Publishing Group}, keywords = {Preclinical research, Reproductive biology}, - pages = {1--8}, file = {Full Text PDF:/home/alex/Zotero/storage/MUPKFK2K/Bull et al. - 2019 - Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles.pdf:application/pdf}, } +@article{pratikno_pdf_2024, + title = {({PDF}) A novel women's ovulation prediction through salivary ferning using the box counting and deep learning}, + url = {https://www.researchgate.net/publication/379467622_A_novel_women's_ovulation_prediction_through_salivary_ferning_using_the_box_counting_and_deep_learning}, + doi = {10.11591/eei.v13i2.5847}, + abstract = {{PDF} {\textbar} There are several methods to predict a woman's ovulation time, including using a calendar system, basal body temperature, ovulation prediction... {\textbar} Find, read and cite all the research you need on {ResearchGate}}, + journaltitle = {{ResearchGate}}, + author = {Pratikno and Ibrahim and Jusak}, + urldate = {2025-02-11}, + date = {2024-12-09}, + langid = {english}, + file = {Full Text:/home/alex/Zotero/storage/MVSBPZQG/2024 - (PDF) A novel women's ovulation prediction through salivary ferning using the box counting and deep.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/8FYLC8YZ/379467622_A_novel_women's_ovulation_prediction_through_salivary_ferning_using_the_box_counting_.html:text/html}, +} + @incollection{holesh_physiology_2025, - address = {Treasure Island (FL)}, - title = {Physiology, {Ovulation}}, - copyright = {Copyright © 2025, StatPearls Publishing LLC.}, + location = {Treasure Island ({FL})}, + title = {Physiology, Ovulation}, + rights = {Copyright © 2025, {StatPearls} Publishing {LLC}.}, url = {http://www.ncbi.nlm.nih.gov/books/NBK441996/}, - abstract = {Ovulation is a physiologic process defined by the rupture of the dominant follicle of the ovary. This releases an egg into the abdominal cavity. It then is taken up by the fimbriae of the fallopian tube where it has the potential to become fertilized. The ovulation process is regulated by fluxing gonadotropic hormone (FSH/LH) levels. Ovulation is the third phase within the larger uterine cycle (ie, menstrual cycle). The follicular release follows the Follicular phase (ie, dominant follicle development) and precedes the luteal phase (ie, maintenance of corpus luteum) that progresses to either endometrial shedding or implantation. Follicular release occurs around 14 days prior to menstruation in a cyclic pattern if the hypothalamic-pituitary-ovarian axis function is well regulated.  Structure Genotypic females (XX) develop two ovaries that sit adjacent to the uterine horns. Each ovary is anchored to the uterus at the medial pole by the utero-ovarian ligament. The lateral ovarian pole is anchored to the pelvic sidewall by the infundibulopelvic ligament (i.e,. suspensory ligament of the ovary), which carries the ovarian artery and vein. Each ovary contains 1 to 2 million primordial follicles that each contain primary oocytes (ie, eggs) that can supply that female with enough follicles until she reaches her fourth or fifth decades of life. These primordial follicles are arrested in prophase I of meiosis until the onset of puberty. At the onset of pubescence, the gonadotropic hormones began to induce the maturation of the primordial follicle, allowing for the completion of meiosis I, forming a secondary follicle. The secondary follicle begins meiosis II, but this phase will not be completed unless that follicle is fertilized. With each ovulatory cycle, the number of follicles decreases, eventually leading to the onset of Menopause or the cessation of ovulatory function. Per each ovulation cycle, the average ovary loses 1,000 follicles to the process of selecting a dominant follicle that will be released. This process accelerates in an age-dependent manner as well. It is also a common thought that the right and left ovaries alternate follicular releases each month. Ovulation is regulated by the fluctuation between the following hormones. Tight regulation and controlled changes between the following hormones are imperative for the development and release of an oocyte into the adnexal uterine structures.   Hormones involved in ovulation include: Gonadotropin-releasing hormone (GnRH) is a tropic peptide hormone made and secreted by the hypothalamus. It is a releasing hormone that stimulates the release of FSH and LH from the anterior pituitary gland through variations in GnRH pulse frequency. Low-frequency GnRH pulses are responsible for FSH secretion, whereas high-frequency pulses are responsible for LH secretion. During the Follicular phase of the Uterine cycle, estrogen secretion causes the Granulosa cells to autonomously increase their own production of estrogen, contributing to elevation in estrogen serum levels. This elevation is communicated to the hypothalamus and contributes to the increase in GnRH pulse frequency, eventually stimulating the LH surge that eventually induces the follicular rupture and release from the corpus luteum and luteinization of the granulosa cells, enabling the synthesis of progesterone in place of estrogen. Finally, the low levels of LH following the surge restart the FSH production by the slow-pulsation frequency of GnRH release. . Gonadotropin hormones are heterodimeric glycoproteins with alpha/beta subunits. The alpha subunit is common to all glycoproteins, including TSH (thyroid-stimulating hormone) and HCG (human chorionic gonadotropin hormone).  The relationship between FSH and LH hormones is responsible for the process that induces follicular development, rupture, release, and endometrial reception or shedding. Disruption in the hormonal communication between the gonadotropin-releasing hormones, gonadotropic hormones, and their receptors can lead to anovulation or amenorrhea, leading to various pathologic sequelae as a consequence. Follicle-Stimulating Hormone (FSH) is a gonadotropin synthesized and secreted from the anterior pituitary gland in response to slow-frequency pulsatile GnRH. FSH stimulates the growth and maturation of immature oocytes into mature (Graafian) secondary follicles before ovulation. FSH Receptors are G-protein coupled receptors and are found in the Granulosa cells that surround developing ovarian follicles. The granulosa cells initially produce the estrogen needed to maturate the developing dominant follicle. After 2 days of sustained elevation of estrogen levels, the LH surge causes luteinization of the granulosa cells into LH receptive cells. This transition enables granulosa cells to respond to LH levels and produce progesterone. : Estrogen is a steroid hormone that is responsible for the growth and regulation of the female reproductive system and secondary sex characteristics. Estrogen is produced by the granulosa cells of the developing follicle and exerts negative feedback on LH production in the early part of the menstrual cycle. However, once estrogen levels reach a critical level as oocytes mature within the ovary in preparation for ovulation, estrogen begins to exert positive feedback on LH production, leading to the LH surge through its effects on GnRH pulse frequency. Estrogen also has many other effects that are important for bone health and cardiovascular health in premenopausal patients, which will be discussed in another article. Luteinizing Hormone (LH) is a gonadotropin synthesized and secreted by the anterior pituitary gland in response to high-frequency GnRH release. LH is responsible for inducing ovulation, preparation for fertilized oocyte uterine implantation, and the ovarian production of progesterone through stimulation of theca cells and luteinized granulosa cells. Prior to the LH surge, LH interacts with Theca cells that are adjacent to granulosa cells in the ovary. These cells produce androgens, which diffuse into the granulosa cells and convert to estrogen for follicular development. The LH surge creates the environment for follicular eruption by increasing the activity of the proteolytic enzymes that weaken the ovarian wall, allowing for the passage of the oocyte. After the oocyte is released, the follicular remnants are theca and luteinized granulosa cells. Their function is now to produce progesterone, which is the hormone responsible for maintaining the uterine environment that can accept a fertilized embryo. Progesterone is a steroid hormone that is responsible for preparing the endometrium for the uterine implantation of the fertilized egg and maintenance of pregnancy. If a fertilized egg implants, the corpus luteum secretes progesterone in early pregnancy until the placenta develops and takes over progesterone production for the remainder of the pregnancy.}, - language = {eng}, - urldate = {2025-03-07}, + abstract = {Ovulation is a physiologic process defined by the rupture of the dominant follicle of the ovary. This releases an egg into the abdominal cavity. It then is taken up by the fimbriae of the fallopian tube where it has the potential to become fertilized. The ovulation process is regulated by fluxing gonadotropic hormone ({FSH}/{LH}) levels. Ovulation is the third phase within the larger uterine cycle (ie, menstrual cycle). The follicular release follows the Follicular phase (ie, dominant follicle development) and precedes the luteal phase (ie, maintenance of corpus luteum) that progresses to either endometrial shedding or implantation. Follicular release occurs around 14 days prior to menstruation in a cyclic pattern if the hypothalamic-pituitary-ovarian axis function is well regulated.  Structure Genotypic females ({XX}) develop two ovaries that sit adjacent to the uterine horns. Each ovary is anchored to the uterus at the medial pole by the utero-ovarian ligament. The lateral ovarian pole is anchored to the pelvic sidewall by the infundibulopelvic ligament (i.e,. suspensory ligament of the ovary), which carries the ovarian artery and vein. Each ovary contains 1 to 2 million primordial follicles that each contain primary oocytes (ie, eggs) that can supply that female with enough follicles until she reaches her fourth or fifth decades of life. These primordial follicles are arrested in prophase I of meiosis until the onset of puberty. At the onset of pubescence, the gonadotropic hormones began to induce the maturation of the primordial follicle, allowing for the completion of meiosis I, forming a secondary follicle. The secondary follicle begins meiosis {II}, but this phase will not be completed unless that follicle is fertilized. With each ovulatory cycle, the number of follicles decreases, eventually leading to the onset of Menopause or the cessation of ovulatory function. Per each ovulation cycle, the average ovary loses 1,000 follicles to the process of selecting a dominant follicle that will be released. This process accelerates in an age-dependent manner as well. It is also a common thought that the right and left ovaries alternate follicular releases each month. Ovulation is regulated by the fluctuation between the following hormones. Tight regulation and controlled changes between the following hormones are imperative for the development and release of an oocyte into the adnexal uterine structures.   Hormones involved in ovulation include: Gonadotropin-releasing hormone ({GnRH}) is a tropic peptide hormone made and secreted by the hypothalamus. It is a releasing hormone that stimulates the release of {FSH} and {LH} from the anterior pituitary gland through variations in {GnRH} pulse frequency. Low-frequency {GnRH} pulses are responsible for {FSH} secretion, whereas high-frequency pulses are responsible for {LH} secretion. During the Follicular phase of the Uterine cycle, estrogen secretion causes the Granulosa cells to autonomously increase their own production of estrogen, contributing to elevation in estrogen serum levels. This elevation is communicated to the hypothalamus and contributes to the increase in {GnRH} pulse frequency, eventually stimulating the {LH} surge that eventually induces the follicular rupture and release from the corpus luteum and luteinization of the granulosa cells, enabling the synthesis of progesterone in place of estrogen. Finally, the low levels of {LH} following the surge restart the {FSH} production by the slow-pulsation frequency of {GnRH} release. . Gonadotropin hormones are heterodimeric glycoproteins with alpha/beta subunits. The alpha subunit is common to all glycoproteins, including {TSH} (thyroid-stimulating hormone) and {HCG} (human chorionic gonadotropin hormone).  The relationship between {FSH} and {LH} hormones is responsible for the process that induces follicular development, rupture, release, and endometrial reception or shedding. Disruption in the hormonal communication between the gonadotropin-releasing hormones, gonadotropic hormones, and their receptors can lead to anovulation or amenorrhea, leading to various pathologic sequelae as a consequence. Follicle-Stimulating Hormone ({FSH}) is a gonadotropin synthesized and secreted from the anterior pituitary gland in response to slow-frequency pulsatile {GnRH}. {FSH} stimulates the growth and maturation of immature oocytes into mature (Graafian) secondary follicles before ovulation. {FSH} Receptors are G-protein coupled receptors and are found in the Granulosa cells that surround developing ovarian follicles. The granulosa cells initially produce the estrogen needed to maturate the developing dominant follicle. After 2 days of sustained elevation of estrogen levels, the {LH} surge causes luteinization of the granulosa cells into {LH} receptive cells. This transition enables granulosa cells to respond to {LH} levels and produce progesterone. : Estrogen is a steroid hormone that is responsible for the growth and regulation of the female reproductive system and secondary sex characteristics. Estrogen is produced by the granulosa cells of the developing follicle and exerts negative feedback on {LH} production in the early part of the menstrual cycle. However, once estrogen levels reach a critical level as oocytes mature within the ovary in preparation for ovulation, estrogen begins to exert positive feedback on {LH} production, leading to the {LH} surge through its effects on {GnRH} pulse frequency. Estrogen also has many other effects that are important for bone health and cardiovascular health in premenopausal patients, which will be discussed in another article. Luteinizing Hormone ({LH}) is a gonadotropin synthesized and secreted by the anterior pituitary gland in response to high-frequency {GnRH} release. {LH} is responsible for inducing ovulation, preparation for fertilized oocyte uterine implantation, and the ovarian production of progesterone through stimulation of theca cells and luteinized granulosa cells. Prior to the {LH} surge, {LH} interacts with Theca cells that are adjacent to granulosa cells in the ovary. These cells produce androgens, which diffuse into the granulosa cells and convert to estrogen for follicular development. The {LH} surge creates the environment for follicular eruption by increasing the activity of the proteolytic enzymes that weaken the ovarian wall, allowing for the passage of the oocyte. After the oocyte is released, the follicular remnants are theca and luteinized granulosa cells. Their function is now to produce progesterone, which is the hormone responsible for maintaining the uterine environment that can accept a fertilized embryo. Progesterone is a steroid hormone that is responsible for preparing the endometrium for the uterine implantation of the fertilized egg and maintenance of pregnancy. If a fertilized egg implants, the corpus luteum secretes progesterone in early pregnancy until the placenta develops and takes over progesterone production for the remainder of the pregnancy.}, booktitle = {{StatPearls}}, - publisher = {StatPearls Publishing}, + publisher = {{StatPearls} Publishing}, author = {Holesh, Julie E. and Bass, Autumn N. and Lord, Megan}, - year = {2025}, + urldate = {2025-03-07}, + date = {2025}, pmid = {28723025}, file = {Printable HTML:/home/alex/Zotero/storage/D6LTGP64/NBK441996.html:text/html}, } @@ -1295,1146 +1071,1276 @@ we have a relatively large dataset, thus tft will be focused on issn = {00150282}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0015028215002034}, doi = {10.1016/j.fertnstert.2015.03.004}, - language = {en}, - number = {5}, - urldate = {2025-03-07}, - journal = {Fertility and Sterility}, - author = {Sauer, Mark V.}, - month = may, - year = {2015}, pages = {1136--1143}, + number = {5}, + journaltitle = {Fertility and Sterility}, + author = {Sauer, Mark V.}, + urldate = {2025-03-07}, + date = {2015-05}, + langid = {english}, file = {PDF:/home/alex/Zotero/storage/W6LCEFP6/Sauer - 2015 - Reproduction at an advanced maternal age and maternal health.pdf:application/pdf}, } -@techreport{mckinsey_health_institute_blueprint_nodate, - title = {Blueprint to {Close} the {Women}’s {Health} {Gap}: {How} to {Improve} {Lives} and {Economies} for {All}}, - shorttitle = {Blueprint to {Close} the {Women}’s {Health} {Gap}}, +@report{mckinsey_health_institute_blueprint_nodate, + title = {Blueprint to Close the Women’s Health Gap: How to Improve Lives and Economies for All}, + shorttitle = {Blueprint to Close the Women’s Health Gap}, + institution = {{McKinsey} Health Institute, World Economic Forum}, + author = {{McKinsey} Health Institute and World Economic Forum}, urldate = {2025-03-07}, - institution = {McKinsey Health Institute, World Economic Forum}, - author = {McKinsey Health Institute and World Economic Forum}, file = {PDF:/home/alex/Zotero/storage/NF6LAC2S/WEF_Blueprint_to_Close_the_Women’s_Health_Gap_2025.pdf:application/pdf}, } -@techreport{mckinsey_health_institute_closing_2024, - title = {Closing the {Women}’s {Health} {Gap}: {A} \$1 {Trillion} {Opportunity} to {Improve} {Lives} and {Economies}}, - shorttitle = {Closing the {Women}’s {Health} {Gap}}, +@report{mckinsey_health_institute_closing_2024, + title = {Closing the Women’s Health Gap: A \$1 Trillion Opportunity to Improve Lives and Economies}, + shorttitle = {Closing the Women’s Health Gap}, + institution = {{McKinsey} Health Institute, World Economic Forum}, + author = {{McKinsey} Health Institute and World Economic Forum}, urldate = {2025-03-07}, - institution = {McKinsey Health Institute, World Economic Forum}, - author = {McKinsey Health Institute and World Economic Forum}, - month = jan, - year = {2024}, + date = {2024-01}, file = {PDF:/home/alex/Zotero/storage/US6TWL7D/closing-the-womens-health-gap-report.pdf:application/pdf}, } @misc{global_burden_of_disease_collaborative_network_global_2020, - title = {Global {Burden} of {Disease} {Study} 2019 ({GBD} 2019) {Disability} {Weights}}, + title = {Global Burden of Disease Study 2019 ({GBD} 2019) Disability Weights}, url = {http://ghdx.healthdata.org/record/ihme-data/gbd-2019-disability-weights}, doi = {10.6069/1W19-VX76}, - abstract = {"The Global Burden of Disease Study 2019 (GBD 2019), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors for 204 countries and territories and selected subnational locations. + abstract = {"The Global Burden of Disease Study 2019 ({GBD} 2019), coordinated by the Institute for Health Metrics and Evaluation ({IHME}), estimated the burden of diseases, injuries, and risk factors for 204 countries and territories and selected subnational locations. -Disability weights, which represent the magnitude of health loss associated with specific health outcomes, are used to calculate years lived with disability (YLD) for these outcomes in a given population. The weights are measured on a scale from 0 to 1, where 0 equals a state of full health and 1 equals death. This table provides disability weights for the 440 health states (including combined health states) used to estimate nonfatal health outcomes for the GBD 2019 study. +Disability weights, which represent the magnitude of health loss associated with specific health outcomes, are used to calculate years lived with disability ({YLD}) for these outcomes in a given population. The weights are measured on a scale from 0 to 1, where 0 equals a state of full health and 1 equals death. This table provides disability weights for the 440 health states (including combined health states) used to estimate nonfatal health outcomes for the {GBD} 2019 study. -For additional GBD results and resources, visit the GBD 2019 Data Resources page."}, - urldate = {2025-03-07}, - publisher = {Institute for Health Metrics and Evaluation (IHME)}, +For additional {GBD} results and resources, visit the {GBD} 2019 Data Resources page."}, + publisher = {Institute for Health Metrics and Evaluation ({IHME})}, author = {{Global Burden of Disease Collaborative Network}}, - year = {2020}, + urldate = {2025-03-07}, + date = {2020}, } @book{noauthor_research_2021, - title = {Research {Funding} for {Women}'s {Health}: {A} {Modeling} {Study} of {Societal} {Impact}: {Findings} for {Alzheimer}'s {Disease} and {Alzheimer}'s {Disease} {Related} {Dementia} {Model}}, - shorttitle = {Research {Funding} for {Women}'s {Health}}, + title = {Research Funding for Women's Health: A Modeling Study of Societal Impact: Findings for Alzheimer's Disease and Alzheimer's Disease Related Dementia Model}, url = {https://www.rand.org/pubs/working_papers/WRA708-1.html}, - language = {en}, + shorttitle = {Research Funding for Women's Health}, + publisher = {{RAND} Corporation}, urldate = {2025-03-07}, - publisher = {RAND Corporation}, - year = {2021}, + date = {2021}, + langid = {english}, doi = {10.7249/WRA708-1}, file = {PDF:/home/alex/Zotero/storage/93NC3JH3/2021 - Research Funding for Women's Health A Modeling Study of Societal Impact Findings for Alzheimer's D.pdf:application/pdf}, } -@misc{noauthor_create_nodate, - title = {Create baseline model - {ValueError}: too many values to unpack (expected 2) · {Issue} \#230 · sktime/pytorch-forecasting}, - shorttitle = {Create baseline model - {ValueError}}, - url = {https://github.com/sktime/pytorch-forecasting/issues/230}, - abstract = {PyTorch-Forecasting version: 0.7.1 PyTorch version: 1.7.1 Python version: 3.7 Operating System: MAC OS Big Sur: Version 11.1 Expected behavior I executed code actuals = torch.cat([y for x, (y, weig...}, - language = {en}, - urldate = {2025-03-19}, - journal = {GitHub}, +@article{dunson_day-specific_1999, + title = {Day-specific probabilities of clinical pregnancy based on two studies with imperfect measures of ovulation}, + volume = {14}, + issn = {1460-2350, 0268-1161}, + url = {https://academic.oup.com/humrep/article-lookup/doi/10.1093/humrep/14.7.1835}, + doi = {10.1093/humrep/14.7.1835}, + pages = {1835--1839}, + number = {7}, + journaltitle = {Human Reproduction}, + author = {Dunson, D.B. and Baird, D.D. and Wilcox, A.J. and Weinberg, C.R.}, + urldate = {2025-03-05}, + date = {1999-07}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/8DE3ZLPJ/Dunson et al. - 1999 - Day-specific probabilities of clinical pregnancy based on two studies with imperfect measures of ovu.pdf:application/pdf}, } -@book{pfannstiel_entrepreneurship_2018, - address = {Wiesbaden}, - title = {Entrepreneurship im {Gesundheitswesen} {II}}, - copyright = {http://www.springer.com/tdm}, - isbn = {978-3-658-14780-8 978-3-658-14781-5}, - url = {http://link.springer.com/10.1007/978-3-658-14781-5}, - language = {de}, - urldate = {2025-03-17}, - publisher = {Springer Fachmedien Wiesbaden}, - editor = {Pfannstiel, Mario A. and Da-Cruz, Patrick and Rasche, Christoph}, - year = {2018}, - doi = {10.1007/978-3-658-14781-5}, - file = {PDF:/home/alex/Zotero/storage/DTVM5NBP/Pfannstiel et al. - 2018 - Entrepreneurship im Gesundheitswesen II.pdf:application/pdf}, +@article{papacharalampous_predictability_2018, + title = {Predictability of monthly temperature and precipitation using automatic time series forecasting methods}, + volume = {66}, + issn = {1895-7455}, + url = {https://doi.org/10.1007/s11600-018-0120-7}, + doi = {10.1007/s11600-018-0120-7}, + abstract = {We investigate the predictability of monthly temperature and precipitation by applying automatic univariate time series forecasting methods to a sample of 985 40-year-long monthly temperature and 1552 40-year-long monthly precipitation time series. The methods include a naïve one based on the monthly values of the last year, as well as the random walk (with drift), {AutoRegressive} Fractionally Integrated Moving Average ({ARFIMA}), exponential smoothing state-space model with Box–Cox transformation, {ARMA} errors, Trend and Seasonal components ({BATS}), simple exponential smoothing, Theta and Prophet methods. Prophet is a recently introduced model inspired by the nature of time series forecasted at Facebook and has not been applied to hydrometeorological time series before, while the use of random walk, {BATS}, simple exponential smoothing and Theta is rare in hydrology. The methods are tested in performing multi-step ahead forecasts for the last 48 months of the data. We further investigate how different choices of handling the seasonality and non-normality affect the performance of the models. The results indicate that: (a) all the examined methods apart from the naïve and random walk ones are accurate enough to be used in long-term applications; (b) monthly temperature and precipitation can be forecasted to a level of accuracy which can barely be improved using other methods; (c) the externally applied classical seasonal decomposition results mostly in better forecasts compared to the automatic seasonal decomposition used by the {BATS} and Prophet methods; and (d) Prophet is competitive, especially when it is combined with externally applied classical seasonal decomposition.}, + pages = {807--831}, + number = {4}, + journaltitle = {Acta Geophys.}, + author = {Papacharalampous, Georgia and Tyralis, Hristos and Koutsoyiannis, Demetris}, + urldate = {2025-03-04}, + date = {2018-08-01}, + langid = {english}, + keywords = {Time series forecasting, {ARFIMA}, Multi-step ahead forecasting, Precipitation forecasting, Prophet, Temperature forecasting}, } -@article{murray_diagnosis_2005, - title = {Diagnosis and treatment of ectopic pregnancy}, - volume = {173}, - issn = {0820-3946, 1488-2329}, - url = {http://www.cmaj.ca/cgi/doi/10.1503/cmaj.050222}, - doi = {10.1503/cmaj.050222}, - abstract = {ECTOPIC PREGNANCY IS A LIFE- AND FERTILITY-threatening condition that is commonly seen in Canadian emergency departments. Increases in the availability and use of hormonal markers, coupled with advances in formal and emergency ultrasonography have changed the diagnostic approach to the patient in the emergency department with first-trimester bleeding or pain. Ultrasonography should be the initial investigation for symptomatic women in their first trimester; when the results are indeterminate, the serum β human chorionic gonadotropin (β-hCG) concentration should be measured. Serial measurement of β-hCG and progesterone concentrations may be useful when the diagnosis remains unclear. Advances in surgical and medical therapy for ectopic pregnancy have allowed the proliferation of minimally invasive or noninvasive treatment. Guidelines for laparoscopy and for methotrexate therapy are provided.}, - language = {en}, - number = {8}, - urldate = {2025-03-17}, - journal = {Canadian Medical Association Journal}, - author = {Murray, H.}, - month = oct, - year = {2005}, - pages = {905--912}, - file = {PDF:/home/alex/Zotero/storage/Y73KE57K/Murray - 2005 - Diagnosis and treatment of ectopic pregnancy.pdf:application/pdf}, +@inproceedings{habib_n-beats_2025, + location = {Cham}, + title = {N-{BEATS} \& Temporal Fusion Transformer Based Surface Temperature Prediction and Forecasting for Realizing Global Warming Trends}, + isbn = {978-3-031-75167-7}, + doi = {10.1007/978-3-031-75167-7_3}, + abstract = {At the pinnacle of civilization, where the impacts of climate change have been increasingly felt, weather prediction plays a critical role in mitigating the potential disasters that may arise. Moreover, with the gradual change on climate, surface temperature of the earth is increasing. This increasing rate of the surface temperature causing global warming which is a matter of intimidation. To leave off this global warming, weather forecasting can be used as an arsenal. Selecting the appropriate tools and models for weather prediction is a crucial step in ensuring accurate forecasts. In this research paper, the focus was on studying the versatility of three specific architectures for weather prediction: {LSTM}, Temporal Fusion Transformer, and N-{BEATS}. To assess these architectures’ performance, we conducted a number of experiments. With the lowest Mean Absolute Error ({MAE}) and Root Mean Square Error ({RMSE}) of the three, {NBEATS} stood out. This shows that when compared to the other models, the N-{BEATS} architecture had greater prediction accuracy. It's vital to remember, too, that the trials also showed that the Temporal Fusion Transformer and {LSTM} performed well. The only distinction was that these models required larger sizes in terms of parameters and computational complexity to achieve their performance levels. Consequently, considering both performance and model size, the researchers determined that N-{BEATS} was the most optimal and versatile architecture for weather prediction. Its ability to achieve excellent results with a smaller model size makes it a favorable choice for practical applications.}, + pages = {30--41}, + booktitle = {Artificial Intelligence and Speech Technology}, + publisher = {Springer Nature Switzerland}, + author = {Habib, Adria Binte and Ashraf, Faisal Bin and Hossain, Muhammad Iqbal and Alam, Golam Rabiul}, + editor = {Dev, Amita and Sharma, Arun and Agrawal, S. S. and Rani, Ritu}, + date = {2025}, + langid = {english}, + keywords = {{LSTM}, N-{BEATS}, Temporal Fusion Transformer, Time Series Analysis, Weather Prediction}, } -@article{rosenfield_adolescent_2013, - title = {Adolescent {Anovulation}: {Maturational} {Mechanisms} and {Implications}}, - volume = {98}, - issn = {0021-972X, 1945-7197}, - shorttitle = {Adolescent {Anovulation}}, - url = {https://academic.oup.com/jcem/article-lookup/doi/10.1210/jc.2013-1770}, - doi = {10.1210/jc.2013-1770}, - language = {en}, - number = {9}, - urldate = {2025-03-17}, - journal = {The Journal of Clinical Endocrinology \& Metabolism}, - author = {Rosenfield, Robert L.}, - month = sep, - year = {2013}, - pages = {3572--3583}, - file = {PDF:/home/alex/Zotero/storage/BEF7PQT6/Rosenfield - 2013 - Adolescent Anovulation Maturational Mechanisms and Implications.pdf:application/pdf}, +@article{wu_interpretable_2022, + title = {Interpretable wind speed prediction with multivariate time series and temporal fusion transformers}, + volume = {252}, + issn = {03605442}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0360544222008933}, + doi = {10.1016/j.energy.2022.123990}, + abstract = {Wind power has been utilized well in power systems, so steady and successful wind speed forecasting is crucial to security management power grid market economy. To date, most researchers have often discounted the interpretability of prediction models, leading to obscure forecasts. This study puts forward a unique forecasting methodology that incorporates notable decomposition techniques, multifactor interpretable forecasting models, and optimization algorithms. In the proposed model, variational mode decomposition is employed to break down the raw wind speed sequence into a set of intrinsic mode functions. Adaptive differential evolution is then used for optimizing several parameters of temporal fusion transformers ({TFT}) to achieve satisfactory forecasting performance. {TFT} is a new attention-based deep learning model that puts together high-performance multi-horizon prediction and interpretable insights into temporal dynamics. Empirical studies using eight real-world 1-h wind speed data sets in Albert, Canada, and Five Points, {USA} demonstrate that the system using the proposed model outperforms those employing other comparable models in nearly all performance metrics. Examples of {TFT}'s interpretable outputs are the importance ranking of the decomposed wind speed sub-sequences and meteorological data and attention analysis of different step lengths. The findings signify substantial progress for wind speed prediction and aid policymakers.}, + pages = {123990}, + journaltitle = {Energy}, + author = {Wu, Binrong and Wang, Lin and Zeng, Yu-Rong}, + urldate = {2025-02-25}, + date = {2022-08}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/MHAFY926/Wu et al. - 2022 - Interpretable wind speed prediction with multivariate time series and temporal fusion transformers.pdf:application/pdf}, } -@book{cryer_time_2008, - address = {New York}, - edition = {2nd ed}, - series = {Springer texts in statistics}, - title = {Time series analysis: with applications in {R}}, - isbn = {978-0-387-75958-6 978-0-387-75959-3}, - shorttitle = {Time series analysis}, - language = {en}, - publisher = {Springer}, - author = {Cryer, Jonathan D. and Chan, Kung-sik}, - year = {2008}, - note = {OCLC: ocn191760003}, - keywords = {Data processing, R (Computer program language), Time-series analysis}, - file = {PDF:/home/alex/Zotero/storage/CIYMBUEW/Cryer and Chan - 2008 - Time series analysis with applications in R.pdf:application/pdf}, +@article{schwenke_show_nodate, + title = {Show Me What You’re Looking For: Visualizing Abstracted Transformer Attention for Enhancing Their Local Interpretability on Time Series Data}, + abstract = {While Transformers have shown their advantages considering their learning performance, their lack of explainability and interpretability is still a major problem. This specifically relates to the processing of time series, as a specific form of complex data. In this paper, we propose an approach for visualizing abstracted information in order to enable computational sensemaking and local interpretability on the respective Transformer model. Our results demonstrate the efficacy of the proposed abstraction method and visualization, utilizing both synthetic and real world data for evaluation.}, + author = {Schwenke, Leonid and Atzmueller, Martin}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/SLVVAAXA/Schwenke and Atzmueller - Show Me What You’re Looking For Visualizing Abstracted Transformer Attention for Enhancing Their Lo.pdf:application/pdf}, } -@book{hamilton_time_1994, - address = {Princeton (N.J.)}, - title = {Time series analysis}, - isbn = {978-0-691-04289-3}, - language = {eng}, - publisher = {Princeton university press}, - author = {Hamilton, James Douglas}, - year = {1994}, - file = {PDF:/home/alex/Zotero/storage/J8GVKKHG/Hamilton - 1994 - Time series analysis.pdf:application/pdf}, +@inproceedings{schwenke_constructing_2021, + title = {Constructing Global Coherence Representations: Identifying Interpretability and Coherences of Transformer Attention in Time Series Data}, + url = {https://ieeexplore.ieee.org/document/9564126/?arnumber=9564126}, + doi = {10.1109/DSAA53316.2021.9564126}, + shorttitle = {Constructing Global Coherence Representations}, + abstract = {Transformer models have shown significant advances recently based on the general concept of Attention — to focus on specifically important and relevant parts of the input data. However, methods for enhancing their interpretability and explainability are still lacking. This is the problem which we tackle in this paper, to make Multi-Headed Attention more interpretable and explainable for time series classification. We present a method for constructing global coherence representations from Multi-Headed Attention of Transformer architectures. Accordingly, we present abstraction and interpretation methods, leading to intuitive visualizations of the respective attention patterns. We evaluate our proposed approach and the presented methods on several datasets demonstrating their efficacy.}, + eventtitle = {2021 {IEEE} 8th International Conference on Data Science and Advanced Analytics ({DSAA})}, + pages = {1--12}, + booktitle = {2021 {IEEE} 8th International Conference on Data Science and Advanced Analytics ({DSAA})}, + author = {Schwenke, Leonid and Atzmueller, Martin}, + urldate = {2025-02-25}, + date = {2021-10}, + keywords = {Time series analysis, Attention, Coherence, Comprehensibility, Conferences, Data science, Data visualization, Deep Learning, Explainability, Global Class Representation, Interpretability, Scalability, Time Series Classification, Transformer, Transformers, Visualization}, + file = {Full Text PDF:/home/alex/Zotero/storage/VRGIDFY8/Schwenke and Atzmueller - 2021 - Constructing Global Coherence Representations Identifying Interpretability and Coherences of Transf.pdf:application/pdf;IEEE Xplore Abstract Record:/home/alex/Zotero/storage/Z45R3XWF/9564126.html:text/html}, } -@misc{noauthor_playtikaosstft-torch_2025, - title = {{PlaytikaOSS}/tft-torch}, - copyright = {MIT}, - url = {https://github.com/PlaytikaOSS/tft-torch}, - abstract = {A Python library that implements ״Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting״}, - urldate = {2025-03-19}, - publisher = {Playtika}, - month = jan, - year = {2025}, - note = {original-date: 2021-11-28T07:08:32Z}, +@incollection{iliadis_temporal_2023, + location = {Cham}, + title = {Temporal Attention Signatures for Interpretable Time-Series Prediction}, + volume = {14259}, + isbn = {978-3-031-44222-3 978-3-031-44223-0}, + url = {https://link.springer.com/10.1007/978-3-031-44223-0_22}, + abstract = {Deep neural networks have become a staple in time-series prediction due to their remarkable accuracy. However, their internal workings often remain elusive. Significant advancements have been made in the interpretability of these networks, with attention mechanisms and feature maps being notably effective for image classification by highlighting the crucial data points. While human observers can readily confirm the significance of features in image classification, the interpretability of time-series data and its modeling remains challenging. To address this, we put forth an innovative approach that unifies temporal attention and visualization as a blend of recurrent neural networks, self-attention, and general attention. This synergy results in the generation of temporal attention signatures, akin to image attention heat maps. Temporal attention not only enhances prediction accuracy beyond that of recurrent networks alone but also demonstrates that varying label classes yield distinct attention signatures. This observation indicates that neural networks focus on different sections of time-series sequences contingent on the prediction target. We conclude with a discussion on the practical implications of this novel approach, including its applicability to model interpretation, sequence length selection, and model validation. This leads to more accurate, robust, and interpretable models, instilling greater confidence in their results.}, + pages = {268--280}, + booktitle = {Artificial Neural Networks and Machine Learning – {ICANN} 2023}, + publisher = {Springer Nature Switzerland}, + author = {Katrompas, Alexander and Metsis, Vangelis}, + editor = {Iliadis, Lazaros and Papaleonidas, Antonios and Angelov, Plamen and Jayne, Chrisina}, + urldate = {2025-02-25}, + date = {2023}, + langid = {english}, + doi = {10.1007/978-3-031-44223-0_22}, + note = {Series Title: Lecture Notes in Computer Science}, + file = {PDF:/home/alex/Zotero/storage/LV7IVKZK/Katrompas and Metsis - 2023 - Temporal Attention Signatures for Interpretable Time-Series Prediction.pdf:application/pdf}, } -@misc{sherar_mattsherartemporal_fusion_transform_2025, - title = {mattsherar/{Temporal}\_Fusion\_Transform}, - url = {https://github.com/mattsherar/Temporal_Fusion_Transform}, - abstract = {Pytorch Implementation of Google's TFT}, - urldate = {2025-03-19}, - author = {Sherar, Matthew}, - month = mar, - year = {2025}, - note = {original-date: 2020-01-11T17:54:01Z}, +@inproceedings{guo_exploring_2019, + title = {Exploring interpretable {LSTM} neural networks over multi-variable data}, + url = {https://proceedings.mlr.press/v97/guo19b.html}, + abstract = {For recurrent neural networks trained on time series with target and exogenous variables, in addition to accurate prediction, it is also desired to provide interpretable insights into the data. In this paper, we explore the structure of {LSTM} recurrent neural networks to learn variable-wise hidden states, with the aim to capture different dynamics in multi-variable time series and distinguish the contribution of variables to the prediction. With these variable-wise hidden states, a mixture attention mechanism is proposed to model the generative process of the target. Then we develop associated training methods to jointly learn network parameters, variable and temporal importance w.r.t the prediction of the target variable. Extensive experiments on real datasets demonstrate enhanced prediction performance by capturing the dynamics of different variables. Meanwhile, we evaluate the interpretation results both qualitatively and quantitatively. It exhibits the prospect as an end-to-end framework for both forecasting and knowledge extraction over multi-variable data.}, + eventtitle = {International Conference on Machine Learning}, + pages = {2494--2504}, + booktitle = {Proceedings of the 36th International Conference on Machine Learning}, + publisher = {{PMLR}}, + author = {Guo, Tian and Lin, Tao and Antulov-Fantulin, Nino}, + urldate = {2025-02-25}, + date = {2019-05-24}, + langid = {english}, + note = {{ISSN}: 2640-3498}, + file = {Full Text PDF:/home/alex/Zotero/storage/VV3I2T4E/Guo et al. - 2019 - Exploring interpretable LSTM neural networks over multi-variable data.pdf:application/pdf;Supplementary PDF:/home/alex/Zotero/storage/57IK29PA/Guo et al. - 2019 - Exploring interpretable LSTM neural networks over multi-variable data.pdf:application/pdf}, } -@misc{noauthor_temporal_nodate, - title = {Temporal {Fusion} {Transformer} ({TFT}) — darts documentation}, - url = {https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html}, - urldate = {2025-03-19}, - file = {Temporal Fusion Transformer (TFT) — darts documentation:/home/alex/Zotero/storage/5QNI6WSL/darts.models.forecasting.tft_model.html:text/html}, +@article{yuan_dcfa-itimenet_2024, + title = {{DCFA}-{iTimeNet}: Dynamic cross-fusion attention network for interpretable time series prediction}, + volume = {55}, + issn = {1573-7497}, + url = {https://doi.org/10.1007/s10489-024-05973-2}, + doi = {10.1007/s10489-024-05973-2}, + shorttitle = {{DCFA}-{iTimeNet}}, + abstract = {Although time series prediction research among engineering and technology has made breakthrough progress in performance, challenges remain in modeling complex dynamic interactions between variables and interpretability. To address these two problems, a novel two-stage strategy framework called {DCFA}-{iTimeNet} is introduced. In the first stage, this paper innovatively proposes a dynamic cross-fusion attention mechanism ({DCFA}) . This module facilitates the model to exchange information between different patches of the time series, thereby capturing the complex interactions between variables across time. In the second stage, we exploit a decomposition-based linear explainable Bidirectional Gated Recurrent Unit ({DeLEBiGRU}), which consists mainly of standard {BiGRU} and tensorized {BiGRU}. It is proposed to analyze each variable’s historical long-term, instantaneous, and future impacts. Such design is crucial for understanding how each variable impacts the overall prediction over time. Extensive experimental results demonstrate that the proposed model can effectively model and interpret complex dynamic relationships of multivariate time series and understand the model’s decision-making process. Moreover, the performance outperforms the state-of-the-art methods.}, + pages = {86}, + number = {2}, + journaltitle = {Appl Intell}, + author = {Yuan, Jianjun and Wu, Fujun and Zhao, Luoming and Pan, Dongbo and Yu, Xinyue}, + urldate = {2025-02-25}, + date = {2024-12-06}, + langid = {english}, + keywords = {Interpretability, Artificial Intelligence, Dynamic cross-fusion attention, Dynamic interaction, Time series prediction}, + file = {Full Text PDF:/home/alex/Zotero/storage/JEXYN7BN/Yuan et al. - 2024 - DCFA-iTimeNet Dynamic cross-fusion attention network for interpretable time series prediction.pdf:application/pdf}, } -@misc{dauphin_language_2017, - title = {Language {Modeling} with {Gated} {Convolutional} {Networks}}, - url = {http://arxiv.org/abs/1612.08083}, - doi = {10.48550/arXiv.1612.08083}, - abstract = {The pre-dominant approach to language modeling to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded context. In this paper we develop a finite context approach through stacked convolutions, which can be more efficient since they allow parallelization over sequential tokens. We propose a novel simplified gating mechanism that outperforms Oord et al (2016) and investigate the impact of key architectural decisions. The proposed approach achieves state-of-the-art on the WikiText-103 benchmark, even though it features long-term dependencies, as well as competitive results on the Google Billion Words benchmark. Our model reduces the latency to score a sentence by an order of magnitude compared to a recurrent baseline. To our knowledge, this is the first time a non-recurrent approach is competitive with strong recurrent models on these large scale language tasks.}, - urldate = {2025-03-26}, - publisher = {arXiv}, - author = {Dauphin, Yann N. and Fan, Angela and Auli, Michael and Grangier, David}, - month = sep, - year = {2017}, - note = {arXiv:1612.08083 [cs]}, - keywords = {Computer Science - Computation and Language}, - file = {Full Text PDF:/home/alex/Zotero/storage/4SBUNZ4A/Dauphin et al. - 2017 - Language Modeling with Gated Convolutional Networks.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/TQBL4EZ7/1612.html:text/html}, -} - -@misc{ba_layer_2016, - title = {Layer {Normalization}}, - url = {http://arxiv.org/abs/1607.06450}, - doi = {10.48550/arXiv.1607.06450}, - abstract = {Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute a mean and variance which are then used to normalize the summed input to that neuron on each training case. This significantly reduces the training time in feed-forward neural networks. However, the effect of batch normalization is dependent on the mini-batch size and it is not obvious how to apply it to recurrent neural networks. In this paper, we transpose batch normalization into layer normalization by computing the mean and variance used for normalization from all of the summed inputs to the neurons in a layer on a single training case. Like batch normalization, we also give each neuron its own adaptive bias and gain which are applied after the normalization but before the non-linearity. Unlike batch normalization, layer normalization performs exactly the same computation at training and test times. It is also straightforward to apply to recurrent neural networks by computing the normalization statistics separately at each time step. Layer normalization is very effective at stabilizing the hidden state dynamics in recurrent networks. Empirically, we show that layer normalization can substantially reduce the training time compared with previously published techniques.}, - urldate = {2025-03-26}, - publisher = {arXiv}, - author = {Ba, Jimmy Lei and Kiros, Jamie Ryan and Hinton, Geoffrey E.}, - month = jul, - year = {2016}, - note = {arXiv:1607.06450 [stat]}, - keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, - file = {Full Text PDF:/home/alex/Zotero/storage/MJWRDPWE/Ba et al. - 2016 - Layer Normalization.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/F9WSU957/1607.html:text/html}, -} - -@misc{clevert_fast_2016, - title = {Fast and {Accurate} {Deep} {Network} {Learning} by {Exponential} {Linear} {Units} ({ELUs})}, - url = {http://arxiv.org/abs/1511.07289}, - doi = {10.48550/arXiv.1511.07289}, - abstract = {We introduce the "exponential linear unit" (ELU) which speeds up learning in deep neural networks and leads to higher classification accuracies. Like rectified linear units (ReLUs), leaky ReLUs (LReLUs) and parametrized ReLUs (PReLUs), ELUs alleviate the vanishing gradient problem via the identity for positive values. However, ELUs have improved learning characteristics compared to the units with other activation functions. In contrast to ReLUs, ELUs have negative values which allows them to push mean unit activations closer to zero like batch normalization but with lower computational complexity. Mean shifts toward zero speed up learning by bringing the normal gradient closer to the unit natural gradient because of a reduced bias shift effect. While LReLUs and PReLUs have negative values, too, they do not ensure a noise-robust deactivation state. ELUs saturate to a negative value with smaller inputs and thereby decrease the forward propagated variation and information. Therefore, ELUs code the degree of presence of particular phenomena in the input, while they do not quantitatively model the degree of their absence. In experiments, ELUs lead not only to faster learning, but also to significantly better generalization performance than ReLUs and LReLUs on networks with more than 5 layers. On CIFAR-100 ELUs networks significantly outperform ReLU networks with batch normalization while batch normalization does not improve ELU networks. ELU networks are among the top 10 reported CIFAR-10 results and yield the best published result on CIFAR-100, without resorting to multi-view evaluation or model averaging. On ImageNet, ELU networks considerably speed up learning compared to a ReLU network with the same architecture, obtaining less than 10\% classification error for a single crop, single model network.}, - urldate = {2025-03-26}, - publisher = {arXiv}, - author = {Clevert, Djork-Arné and Unterthiner, Thomas and Hochreiter, Sepp}, - month = feb, - year = {2016}, - note = {arXiv:1511.07289 [cs]}, +@misc{sprang_enforcing_2024, + title = {Enforcing Interpretability in Time Series Transformers: A Concept Bottleneck Framework}, + url = {http://arxiv.org/abs/2410.06070}, + doi = {10.48550/arXiv.2410.06070}, + shorttitle = {Enforcing Interpretability in Time Series Transformers}, + abstract = {There has been a recent push of research on Transformer-based models for long-term time series forecasting, even though they are inherently difficult to interpret and explain. While there is a large body of work on interpretability methods for various domains and architectures, the interpretability of Transformer-based forecasting models remains largely unexplored. To address this gap, we develop a framework based on Concept Bottleneck Models to enforce interpretability of time series Transformers. We modify the training objective to encourage a model to develop representations similar to predefined interpretable concepts. In our experiments, we enforce similarity using Centered Kernel Alignment, and the predefined concepts include time features and an interpretable, autoregressive surrogate model ({AR}). We apply the framework to the Autoformer model, and present an in-depth analysis for a variety of benchmark tasks. We find that the model performance remains mostly unaffected, while the model shows much improved interpretability. Additionally, interpretable concepts become local, which makes the trained model easily intervenable. As a proof of concept, we demonstrate a successful intervention in the scenario of a time shift in the data, which eliminates the need to retrain.}, + number = {{arXiv}:2410.06070}, + publisher = {{arXiv}}, + author = {Sprang, Angela van and Acar, Erman and Zuidema, Willem}, + urldate = {2025-02-25}, + date = {2024-10-08}, + eprinttype = {arxiv}, + eprint = {2410.06070 [cs]}, keywords = {Computer Science - Machine Learning}, - annote = {Comment: Published as a conference paper at ICLR 2016}, - file = {Full Text PDF:/home/alex/Zotero/storage/PU3ZGP4G/Clevert et al. - 2016 - Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/3MAW2IWE/1511.html:text/html}, + file = {Preprint PDF:/home/alex/Zotero/storage/HVUXXRXJ/Sprang et al. - 2024 - Enforcing Interpretability in Time Series Transformers A Concept Bottleneck Framework.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/6QS78DZP/2410.html:text/html}, } -@article{wang_systematic_2022, - title = {A {Systematic} {Review} of {Time} {Series} {Classification} {Techniques} {Used} in {Biomedical} {Applications}}, - volume = {22}, - issn = {1424-8220}, - url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9611376/}, - doi = {10.3390/s22208016}, - abstract = {Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on PubMed, as well as the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful.}, - number = {20}, - urldate = {2025-05-06}, - journal = {Sensors (Basel, Switzerland)}, - author = {Wang, Will Ke and Chen, Ina and Hershkovich, Leeor and Yang, Jiamu and Shetty, Ayush and Singh, Geetika and Jiang, Yihang and Kotla, Aditya and Shang, Jason Zisheng and Yerrabelli, Rushil and Roghanizad, Ali R. and Shandhi, Md Mobashir Hasan and Dunn, Jessilyn}, - month = oct, - year = {2022}, - pmid = {36298367}, - pmcid = {PMC9611376}, - pages = {8016}, - file = {Full Text PDF:/home/alex/Zotero/storage/2E8EIVER/Wang et al. - 2022 - A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications.pdf:application/pdf}, +@misc{chefer_transformer_2021, + title = {Transformer Interpretability Beyond Attention Visualization}, + url = {http://arxiv.org/abs/2012.09838}, + doi = {10.48550/arXiv.2012.09838}, + abstract = {Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks. In order to visualize the parts of the image that led to a certain classification, existing methods either rely on the obtained attention maps or employ heuristic propagation along the attention graph. In this work, we propose a novel way to compute relevancy for Transformer networks. The method assigns local relevance based on the Deep Taylor Decomposition principle and then propagates these relevancy scores through the layers. This propagation involves attention layers and skip connections, which challenge existing methods. Our solution is based on a specific formulation that is shown to maintain the total relevancy across layers. We benchmark our method on very recent visual Transformer networks, as well as on a text classification problem, and demonstrate a clear advantage over the existing explainability methods.}, + number = {{arXiv}:2012.09838}, + publisher = {{arXiv}}, + author = {Chefer, Hila and Gur, Shir and Wolf, Lior}, + urldate = {2025-02-25}, + date = {2021-04-05}, + eprinttype = {arxiv}, + eprint = {2012.09838 [cs]}, + keywords = {Computer Science - Computer Vision and Pattern Recognition}, + file = {Preprint PDF:/home/alex/Zotero/storage/3FRISAP7/Chefer et al. - 2021 - Transformer Interpretability Beyond Attention Visualization.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/QUPBJPC9/2012.html:text/html}, } -@article{gharehbaghi_deep_2018, - title = {A {Deep} {Machine} {Learning} {Method} for {Classifying} {Cyclic} {Time} {Series} of {Biological} {Signals} {Using} {Time}-{Growing} {Neural} {Network}}, - volume = {29}, - copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html}, - issn = {2162-237X, 2162-2388}, - url = {https://ieeexplore.ieee.org/document/8066455/}, - doi = {10.1109/TNNLS.2017.2754294}, - number = {9}, - urldate = {2025-05-06}, - journal = {IEEE Transactions on Neural Networks and Learning Systems}, - author = {Gharehbaghi, Arash and Linden, Maria}, - month = sep, - year = {2018}, - pages = {4102--4115}, +@misc{helbling_conceptattention_2025, + title = {{ConceptAttention}: Diffusion Transformers Learn Highly Interpretable Features}, + url = {http://arxiv.org/abs/2502.04320}, + doi = {10.48550/arXiv.2502.04320}, + shorttitle = {{ConceptAttention}}, + abstract = {Do the rich representations of multi-modal diffusion transformers ({DiTs}) exhibit unique properties that enhance their interpretability? We introduce {ConceptAttention}, a novel method that leverages the expressive power of {DiT} attention layers to generate high-quality saliency maps that precisely locate textual concepts within images. Without requiring additional training, {ConceptAttention} repurposes the parameters of {DiT} attention layers to produce highly contextualized concept embeddings, contributing the major discovery that performing linear projections in the output space of {DiT} attention layers yields significantly sharper saliency maps compared to commonly used cross-attention mechanisms. Remarkably, {ConceptAttention} even achieves state-of-the-art performance on zero-shot image segmentation benchmarks, outperforming 11 other zero-shot interpretability methods on the {ImageNet}-Segmentation dataset and on a single-class subset of {PascalVOC}. Our work contributes the first evidence that the representations of multi-modal {DiT} models like Flux are highly transferable to vision tasks like segmentation, even outperforming multi-modal foundation models like {CLIP}.}, + number = {{arXiv}:2502.04320}, + publisher = {{arXiv}}, + author = {Helbling, Alec and Meral, Tuna Han Salih and Hoover, Ben and Yanardag, Pinar and Chau, Duen Horng}, + urldate = {2025-02-25}, + date = {2025-02-06}, + eprinttype = {arxiv}, + eprint = {2502.04320 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition}, + file = {Preprint PDF:/home/alex/Zotero/storage/AEEDM4ZW/Helbling et al. - 2025 - ConceptAttention Diffusion Transformers Learn Highly Interpretable Features.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/SAHGAINH/2502.html:text/html}, } -@article{masini_machine_2023, - title = {Machine learning advances for time series forecasting}, +@article{noauthor_temporal_2021, + title = {Temporal Fusion Transformers for interpretable multi-horizon time series forecasting}, volume = {37}, - issn = {0950-0804, 1467-6419}, - url = {https://onlinelibrary.wiley.com/doi/10.1111/joes.12429}, - doi = {10.1111/joes.12429}, - abstract = {Abstract - In this paper, we survey the most recent advances in supervised machine learning (ML) and high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods, we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feedforward and recurrent versions, and tree‐based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of ML in economics and finance and provide an illustration with high‐frequency financial data.}, - language = {en}, - number = {1}, - urldate = {2025-05-06}, - journal = {Journal of Economic Surveys}, - author = {Masini, Ricardo P. and Medeiros, Marcelo C. and Mendes, Eduardo F.}, - month = feb, - year = {2023}, - pages = {76--111}, - file = {Submitted Version:/home/alex/Zotero/storage/4TZJJUSU/Masini et al. - 2023 - Machine learning advances for time series forecasting.pdf:application/pdf}, + issn = {0169-2070}, + url = {https://www.sciencedirect.com/science/article/pii/S0169207021000637}, + doi = {10.1016/j.ijforecast.2021.03.012}, + abstract = {Multi-horizon forecasting often contains a complex mix of inputs – including static (i.e. time-invariant) covariates, known future inputs, and other e…}, + pages = {1748--1764}, + number = {4}, + journaltitle = {International Journal of Forecasting}, + urldate = {2025-02-24}, + date = {2021-10-01}, + langid = {american}, + note = {Publisher: Elsevier}, + file = {Snapshot:/home/alex/Zotero/storage/SFYESIWK/S0169207021000637.html:text/html;Submitted Version:/home/alex/Zotero/storage/A9AYS5UI/2021 - Temporal Fusion Transformers for interpretable multi-horizon time series forecasting.pdf:application/pdf}, } -@article{wang_systematic_2022-1, - title = {A {Systematic} {Review} of {Time} {Series} {Classification} {Techniques} {Used} in {Biomedical} {Applications}}, - volume = {22}, - copyright = {https://creativecommons.org/licenses/by/4.0/}, - issn = {1424-8220}, - url = {https://www.mdpi.com/1424-8220/22/20/8016}, - doi = {10.3390/s22208016}, - abstract = {Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on PubMed, as well as the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful.}, - language = {en}, - number = {20}, - urldate = {2025-05-06}, - journal = {Sensors}, - author = {Wang, Will Ke and Chen, Ina and Hershkovich, Leeor and Yang, Jiamu and Shetty, Ayush and Singh, Geetika and Jiang, Yihang and Kotla, Aditya and Shang, Jason Zisheng and Yerrabelli, Rushil and Roghanizad, Ali R. and Shandhi, Md Mobashir Hasan and Dunn, Jessilyn}, - month = oct, - year = {2022}, - pages = {8016}, - file = {PDF:/home/alex/Zotero/storage/H5LLUB5K/Wang et al. - 2022 - A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications.pdf:application/pdf}, +@inproceedings{vaswani_attention_2017, + title = {Attention is All you Need}, + volume = {30}, + url = {https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html}, + abstract = {The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms. We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train. Our single model with 165 million parameters, achieves 27.5 {BLEU} {onEnglish}-to-German translation, improving over the existing best ensemble result by over 1 {BLEU}. On English-to-French translation, we outperform the previoussingle state-of-the-art with model by 0.7 {BLEU}, achieving a {BLEU} score of 41.1.}, + booktitle = {Advances in Neural Information Processing Systems}, + publisher = {Curran Associates, Inc.}, + author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, Ł ukasz and Polosukhin, Illia}, + urldate = {2025-02-24}, + date = {2017}, + file = {Full Text PDF:/home/alex/Zotero/storage/MU7NU9LR/Vaswani et al. - 2017 - Attention is All you Need.pdf:application/pdf}, } -@article{leon-lopez_anomaly_2022, - title = {Anomaly {Detection} and {Classification} in {Multispectral} {Time} {Series} {Based} on {Hidden} {Markov} {Models}}, - volume = {60}, - issn = {1558-0644}, - url = {https://ieeexplore.ieee.org/abstract/document/9509347}, - doi = {10.1109/TGRS.2021.3101127}, - abstract = {Monitoring agriculture from satellite remote sensing data, such as multispectral images, has become a powerful tool since it has demonstrated a great potential for providing timely and accurate knowledge of crops. Detecting anomalies in time series of multispectral remote sensing images for crop monitoring is generally performed using a large sample of historical data at a pixel level. Conversely, this article presents a framework for anomaly detection (AD), localization, and classification that exploits the temporal information contained in a given season at a parcel level to detect and localize outliers using hidden Markov models (HMMs). Specifically, the AD part is based on the learning of HMM parameters associated with unlabeled normal data that are used in a second step to detect abnormal crop parcels referred to as anomalies. The learned HMM can also be used in time segments to temporally localize the anomalies affecting the crop parcels. The detected and localized anomalies are finally classified using a supervised classifier, e.g., based on support vector machines. The proposed framework is applicable to images partially covered by clouds and can handle a set of crop parcels acquired in the same season bypassing problems due to crop rotations. Numerical experiments are conducted on synthetic and real data, where the real data correspond to vegetation indices extracted from several multitemporal Sentinel-2 images of rapeseed crops. The proposed approach is compared to standard AD methods yielding better detection rates with the advantage of allowing anomalies to be localized and characterized.}, - urldate = {2025-05-06}, - journal = {IEEE Transactions on Geoscience and Remote Sensing}, - author = {León-López, Kareth M. and Mouret, Florian and Arguello, Henry and Tourneret, Jean-Yves}, - year = {2022}, - keywords = {Hidden Markov models, Time series analysis, Agricultural monitoring, Agriculture, anomaly classification, Anomaly detection, anomaly detection (AD), Feature extraction, hidden Markov models (HMMs), Monitoring, remote sensing, time series, Vegetation mapping}, - pages = {1--11}, - file = {Snapshot:/home/alex/Zotero/storage/HIWT8MDH/9509347.html:text/html;Submitted Version:/home/alex/Zotero/storage/S4K2XXHN/León-López et al. - 2022 - Anomaly Detection and Classification in Multispectral Time Series Based on Hidden Markov Models.pdf:application/pdf}, +@online{noauthor_vivosens_nodate, + title = {vivosens medical gmbh}, + url = {https://www.vivosensmedical.com/}, + urldate = {2025-02-24}, + file = {vivosensmedical.com:/home/alex/Zotero/storage/KSM7HHBJ/www.vivosensmedical.com.html:text/html}, } -@inproceedings{hsieh_explainable_2021, - address = {Virtual Event Israel}, - title = {Explainable {Multivariate} {Time} {Series} {Classification}: {A} {Deep} {Neural} {Network} {Which} {Learns} to {Attend} to {Important} {Variables} {As} {Well} {As} {Time} {Intervals}}, - isbn = {978-1-4503-8297-7}, - shorttitle = {Explainable {Multivariate} {Time} {Series} {Classification}}, - url = {https://dl.acm.org/doi/10.1145/3437963.3441815}, - doi = {10.1145/3437963.3441815}, - language = {en}, - urldate = {2025-05-06}, - booktitle = {Proceedings of the 14th {ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining}}, - publisher = {ACM}, - author = {Hsieh, Tsung-Yu and Wang, Suhang and Sun, Yiwei and Honavar, Vasant}, - month = mar, - year = {2021}, - pages = {607--615}, +@inproceedings{rigotti_attention-based_2021, + title = {Attention-based Interpretability with Concept Transformers}, + url = {https://openreview.net/forum?id=kAa9eDS0RdO}, + abstract = {Attention is a mechanism that has been instrumental in driving remarkable performance gains of deep neural network models in a host of visual, {NLP} and multimodal tasks. One additional notable aspect of attention is that it conveniently exposes the ``reasoning'' behind each particular output generated by the model. Specifically, attention scores over input regions or intermediate features have been interpreted as a measure of the contribution of the attended element to the model inference. While the debate in regard to the interpretability of attention is still not settled, researchers have pointed out the existence of architectures and scenarios that afford a meaningful interpretation of the attention mechanism. Here we propose the generalization of attention from low-level input features to high-level concepts as a mechanism to ensure the interpretability of attention scores within a given application domain. In particular, we design the {ConceptTransformer}, a deep learning module that exposes explanations of the output of a model in which it is embedded in terms of attention over user-defined high-level concepts. Such explanations are {\textbackslash}emph\{plausible\} (i.e.{\textbackslash} convincing to the human user) and {\textbackslash}emph\{faithful\} (i.e.{\textbackslash} truly reflective of the reasoning process of the model). Plausibility of such explanations is obtained by construction by training the attention heads to conform with known relations between inputs, concepts and outputs dictated by domain knowledge. Faithfulness is achieved by design by enforcing a linear relation between the transformer value vectors that represent the concepts and their contribution to the classification log-probabilities. We validate our {ConceptTransformer} module on established explainability benchmarks and show how it can be used to infuse domain knowledge into classifiers to improve accuracy, and conversely to extract concept-based explanations of classification outputs. Code to reproduce our results is available at: {\textbackslash}url\{https://github.com/ibm/concept\_transformer\}.}, + eventtitle = {International Conference on Learning Representations}, + author = {Rigotti, Mattia and Miksovic, Christoph and Giurgiu, Ioana and Gschwind, Thomas and Scotton, Paolo}, + urldate = {2025-02-21}, + date = {2021-10-06}, + langid = {english}, + file = {Full Text PDF:/home/alex/Zotero/storage/U2FUGVF6/Rigotti et al. - 2021 - Attention-based Interpretability with Concept Transformers.pdf:application/pdf}, } -@misc{saluja_towards_2021, - title = {Towards a {Rigorous} {Evaluation} of {Explainability} for {Multivariate} {Time} {Series}}, - url = {http://arxiv.org/abs/2104.04075}, - doi = {10.48550/arXiv.2104.04075}, - abstract = {Machine learning-based systems are rapidly gaining popularity and in-line with that there has been a huge research surge in the field of explainability to ensure that machine learning models are reliable, fair, and can be held liable for their decision-making process. Explainable Artificial Intelligence (XAI) methods are typically deployed to debug black-box machine learning models but in comparison to tabular, text, and image data, explainability in time series is still relatively unexplored. The aim of this study was to achieve and evaluate model agnostic explainability in a time series forecasting problem. This work focused on proving a solution for a digital consultancy company aiming to find a data-driven approach in order to understand the effect of their sales related activities on the sales deals closed. The solution involved framing the problem as a time series forecasting problem to predict the sales deals and the explainability was achieved using two novel model agnostic explainability techniques, Local explainable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP) which were evaluated using human evaluation of explainability. The results clearly indicate that the explanations produced by LIME and SHAP greatly helped lay humans in understanding the predictions made by the machine learning model. The presented work can easily be extended to any time}, - urldate = {2025-05-06}, - publisher = {arXiv}, - author = {Saluja, Rohit and Malhi, Avleen and Knapič, Samanta and Främling, Kary and Cavdar, Cicek}, - month = apr, - year = {2021}, - note = {arXiv:2104.04075 [cs]}, - keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, - annote = {Comment: Journal}, - file = {Preprint PDF:/home/alex/Zotero/storage/6XB73Y2F/Saluja et al. - 2021 - Towards a Rigorous Evaluation of Explainability for Multivariate Time Series.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/CDCGX8EZ/2104.html:text/html}, +@article{kitada_attention_2021, + title = {Attention Meets Perturbations: Robust and Interpretable Attention With Adversarial Training}, + volume = {9}, + issn = {2169-3536}, + url = {https://ieeexplore.ieee.org/abstract/document/9467291}, + doi = {10.1109/ACCESS.2021.3093456}, + shorttitle = {Attention Meets Perturbations}, + abstract = {Although attention mechanisms have been applied to a variety of deep learning models and have been shown to improve the prediction performance, it has been reported to be vulnerable to perturbations to the mechanism. To overcome the vulnerability to perturbations in the mechanism, we are inspired by adversarial training ({AT}), which is a powerful regularization technique for enhancing the robustness of the models. In this paper, we propose a general training technique for natural language processing tasks, including {AT} for attention (Attention {AT}) and more interpretable {AT} for attention (Attention {iAT}). The proposed techniques improved the prediction performance and the model interpretability by exploiting the mechanisms with {AT}. In particular, Attention {iAT} boosts those advantages by introducing adversarial perturbation, which enhances the difference in the attention of the sentences. Evaluation experiments with ten open datasets revealed that {AT} for attention mechanisms, especially Attention {iAT}, demonstrated (1) the best performance in nine out of ten tasks and (2) more interpretable attention (i.e., the resulting attention correlated more strongly with gradient-based word importance) for all tasks. Additionally, the proposed techniques are (3) much less dependent on perturbation size in {AT}.}, + pages = {92974--92985}, + journaltitle = {{IEEE} Access}, + author = {Kitada, Shunsuke and Iyatomi, Hitoshi}, + urldate = {2025-02-21}, + date = {2021}, + note = {Conference Name: {IEEE} Access}, + keywords = {Predictive models, adversarial training, attention mechanism, binary classification, interpretability, Knowledge discovery, natural language inference, Natural language processing, Perturbation methods, question answering, Robustness, Solid modeling, Task analysis, Training}, + file = {Full Text PDF:/home/alex/Zotero/storage/BUXFCXRU/Kitada and Iyatomi - 2021 - Attention Meets Perturbations Robust and Interpretable Attention With Adversarial Training.pdf:application/pdf;IEEE Xplore Abstract Record:/home/alex/Zotero/storage/ZNQD3FRW/9467291.html:text/html}, } -@misc{wang_timexer_2024, - title = {{TimeXer}: {Empowering} {Transformers} for {Time} {Series} {Forecasting} with {Exogenous} {Variables}}, - shorttitle = {{TimeXer}}, - url = {http://arxiv.org/abs/2402.19072}, - doi = {10.48550/arXiv.2402.19072}, - abstract = {Deep models have demonstrated remarkable performance in time series forecasting. However, due to the partially-observed nature of real-world applications, solely focusing on the target of interest, so-called endogenous variables, is usually insufficient to guarantee accurate forecasting. Notably, a system is often recorded into multiple variables, where the exogenous variables can provide valuable external information for endogenous variables. Thus, unlike well-established multivariate or univariate forecasting paradigms that either treat all the variables equally or ignore exogenous information, this paper focuses on a more practical setting: time series forecasting with exogenous variables. We propose a novel approach, TimeXer, to ingest external information to enhance the forecasting of endogenous variables. With deftly designed embedding layers, TimeXer empowers the canonical Transformer with the ability to reconcile endogenous and exogenous information, where patch-wise self-attention and variate-wise cross-attention are used simultaneously. Moreover, global endogenous tokens are learned to effectively bridge the causal information underlying exogenous series into endogenous temporal patches. Experimentally, TimeXer achieves consistent state-of-the-art performance on twelve real-world forecasting benchmarks and exhibits notable generality and scalability. Code is available at this repository: https://github.com/thuml/TimeXer.}, - urldate = {2025-05-09}, - publisher = {arXiv}, - author = {Wang, Yuxuan and Wu, Haixu and Dong, Jiaxiang and Qin, Guo and Zhang, Haoran and Liu, Yong and Qiu, Yunzhong and Wang, Jianmin and Long, Mingsheng}, - month = nov, - year = {2024}, - note = {arXiv:2402.19072 [cs]}, - keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, - file = {Full Text PDF:/home/alex/Zotero/storage/76BQWVIW/Wang et al. - 2024 - TimeXer Empowering Transformers for Time Series Forecasting with Exogenous Variables.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/JL64E9YL/2402.html:text/html}, -} - -@misc{zeng_are_2022, - title = {Are {Transformers} {Effective} for {Time} {Series} {Forecasting}?}, - url = {http://arxiv.org/abs/2205.13504}, - doi = {10.48550/arXiv.2205.13504}, - abstract = {Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the elements in a long sequence. However, in time series modeling, we are to extract the temporal relations in an ordered set of continuous points. While employing positional encoding and using tokens to embed sub-series in Transformers facilitate preserving some ordering information, the nature of the {\textbackslash}emph\{permutation-invariant\} self-attention mechanism inevitably results in temporal information loss. To validate our claim, we introduce a set of embarrassingly simple one-layer linear models named LTSF-Linear for comparison. Experimental results on nine real-life datasets show that LTSF-Linear surprisingly outperforms existing sophisticated Transformer-based LTSF models in all cases, and often by a large margin. Moreover, we conduct comprehensive empirical studies to explore the impacts of various design elements of LTSF models on their temporal relation extraction capability. We hope this surprising finding opens up new research directions for the LTSF task. We also advocate revisiting the validity of Transformer-based solutions for other time series analysis tasks (e.g., anomaly detection) in the future. Code is available at: {\textbackslash}url\{https://github.com/cure-lab/LTSF-Linear\}.}, - urldate = {2025-05-09}, - publisher = {arXiv}, - author = {Zeng, Ailing and Chen, Muxi and Zhang, Lei and Xu, Qiang}, - month = aug, - year = {2022}, - note = {arXiv:2205.13504 [cs]}, - keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, - annote = {Comment: Code is available at https://github.com/cure-lab/LTSF-Linear}, - file = {Full Text PDF:/home/alex/Zotero/storage/V9E95F7E/Zeng et al. - 2022 - Are Transformers Effective for Time Series Forecasting.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/HNVIZG98/2205.html:text/html}, -} - -@article{barrera-animas_rainfall_2022, - title = {Rainfall prediction: {A} comparative analysis of modern machine learning algorithms for time-series forecasting}, - volume = {7}, - issn = {26668270}, - shorttitle = {Rainfall prediction}, - url = {https://linkinghub.elsevier.com/retrieve/pii/S266682702100102X}, - doi = {10.1016/j.mlwa.2021.100204}, - abstract = {Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statistical models that are often too costly, both computationally and budgetary, or are not applied to downstream applications. Therefore, approaches that use Machine Learning algorithms in conjunction with time-series data are being explored as an alternative to overcome these drawbacks. To this end, this study presents a comparative analysis using simplified rainfall estimation models based on conventional Machine Learning algorithms and Deep Learning architectures that are efficient for these downstream applications. Models based on LSTM, Stacked-LSTM, Bidirectional-LSTM Networks, XGBoost, and an ensemble of Gradient Boosting Regressor, Linear Support Vector Regression, and an Extra-trees Regressor were compared in the task of forecasting hourly rainfall volumes using time-series data. Climate data from 2000 to 2020 from five major cities in the United Kingdom were used. The evaluation metrics of Loss, Root Mean Squared Error, Mean Absolute Error, and Root Mean Squared Logarithmic Error were used to evaluate the models’ performance. Results show that a Bidirectional-LSTM Network can be used as a rainfall forecast model with comparable performance to Stacked-LSTM Networks. Among all the models tested, the StackedLSTM Network with two hidden layers and the Bidirectional-LSTM Network performed best. This suggests that models based on LSTM-Networks with fewer hidden layers perform better for this approach; denoting its ability to be applied as an approach for budget-wise rainfall forecast applications.}, - language = {en}, - urldate = {2025-05-08}, - journal = {Machine Learning with Applications}, - author = {Barrera-Animas, Ari Yair and Oyedele, Lukumon O. and Bilal, Muhammad and Akinosho, Taofeek Dolapo and Delgado, Juan Manuel Davila and Akanbi, Lukman Adewale}, - month = mar, - year = {2022}, - pages = {100204}, - file = {PDF:/home/alex/Zotero/storage/FSXMH5N9/Barrera-Animas et al. - 2022 - Rainfall prediction A comparative analysis of modern machine learning algorithms for time-series fo.pdf:application/pdf}, -} - -@article{ahmed_empirical_2010, - title = {An {Empirical} {Comparison} of {Machine} {Learning} {Models} for {Time} {Series} {Forecasting}}, +@inproceedings{choi_retain_2016, + title = {{RETAIN}: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism}, volume = {29}, - issn = {0747-4938, 1532-4168}, - url = {http://www.tandfonline.com/doi/abs/10.1080/07474938.2010.481556}, - doi = {10.1080/07474938.2010.481556}, - language = {en}, - number = {5-6}, - urldate = {2025-05-08}, - journal = {Econometric Reviews}, - author = {Ahmed, Nesreen K. and Atiya, Amir F. and Gayar, Neamat El and El-Shishiny, Hisham}, - month = aug, - year = {2010}, - pages = {594--621}, + url = {https://proceedings.neurips.cc/paper/2016/hash/231141b34c82aa95e48810a9d1b33a79-Abstract.html}, + shorttitle = {{RETAIN}}, + abstract = {Accuracy and interpretability are two dominant features of successful predictive models. Typically, a choice must be made in favor of complex black box models such as recurrent neural networks ({RNN}) for accuracy versus less accurate but more interpretable traditional models such as logistic regression. This tradeoff poses challenges in medicine where both accuracy and interpretability are important. We addressed this challenge by developing the {REverse} Time {AttentIoN} model ({RETAIN}) for application to Electronic Health Records ({EHR}) data. {RETAIN} achieves high accuracy while remaining clinically interpretable and is based on a two-level neural attention model that detects influential past visits and significant clinical variables within those visits (e.g. key diagnoses). {RETAIN} mimics physician practice by attending the {EHR} data in a reverse time order so that recent clinical visits are likely to receive higher attention. {RETAIN} was tested on a large health system {EHR} dataset with 14 million visits completed by 263K patients over an 8 year period and demonstrated predictive accuracy and computational scalability comparable to state-of-the-art methods such as {RNN}, and ease of interpretability comparable to traditional models.}, + booktitle = {Advances in Neural Information Processing Systems}, + publisher = {Curran Associates, Inc.}, + author = {Choi, Edward and Bahadori, Mohammad Taha and Sun, Jimeng and Kulas, Joshua and Schuetz, Andy and Stewart, Walter}, + urldate = {2025-02-21}, + date = {2016}, + file = {Full Text PDF:/home/alex/Zotero/storage/XQLMYHUU/Choi et al. - 2016 - RETAIN An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism.pdf:application/pdf}, } -@article{garcia_prediction_1981, - title = {Prediction of the {Time} of {Ovulation}*}, - volume = {36}, - issn = {0015-0282}, - url = {https://www.sciencedirect.com/science/article/pii/S0015028216457304}, - doi = {10.1016/S0015-0282(16)45730-4}, - abstract = {Prediction of ovulation was established by correlation of clinical parameters, follicular development by ultrasound, and estradiol, progesterone, and luteinizing hormone (LH) determination in 71 menstrual cycles. Laparoscopic follicular aspiration was accomplished in 41 of those cycles. A 28-hour interval from the ascending limb of the LH seems to be the “ideal time” for retrieval of a preovulatory oocyte. The variability in the amount of LH to which the follicle is exposed during the LH surge seems to indicate that there is a relatively low specific value necessary for ovulation. Ovulation occurs approximately 10 ± 5 hours from the LH peak. Progesterone occurs in relation to the LH surge and is helpful for the retrospective analysis of the menstrual cycle.}, +@misc{serrano_is_2019, + title = {Is Attention Interpretable?}, + url = {http://arxiv.org/abs/1906.03731}, + doi = {10.48550/arXiv.1906.03731}, + abstract = {Attention mechanisms have recently boosted performance on a range of {NLP} tasks. Because attention layers explicitly weight input components' representations, it is also often assumed that attention can be used to identify information that models found important (e.g., specific contextualized word tokens). We test whether that assumption holds by manipulating attention weights in already-trained text classification models and analyzing the resulting differences in their predictions. While we observe some ways in which higher attention weights correlate with greater impact on model predictions, we also find many ways in which this does not hold, i.e., where gradient-based rankings of attention weights better predict their effects than their magnitudes. We conclude that while attention noisily predicts input components' overall importance to a model, it is by no means a fail-safe indicator.}, + number = {{arXiv}:1906.03731}, + publisher = {{arXiv}}, + author = {Serrano, Sofia and Smith, Noah A.}, + urldate = {2025-02-21}, + date = {2019-06-09}, + eprinttype = {arxiv}, + eprint = {1906.03731 [cs]}, + keywords = {Computer Science - Computation and Language}, + file = {Preprint PDF:/home/alex/Zotero/storage/DFZ28RG8/Serrano and Smith - 2019 - Is Attention Interpretable.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/63B9XS2Y/1906.html:text/html}, +} + +@article{lyzwinski_innovative_2024, + title = {Innovative Approaches to Menstruation and Fertility Tracking Using Wearable Reproductive Health Technology: Systematic Review}, + volume = {26}, + url = {https://www.jmir.org/2024/1/e45139}, + doi = {10.2196/45139}, + shorttitle = {Innovative Approaches to Menstruation and Fertility Tracking Using Wearable Reproductive Health Technology}, + abstract = {Background: Emerging digital health technology has moved into the reproductive health market for female individuals. In the past, mobile health apps have been used to monitor the menstrual cycle using manual entry. New technological trends involve the use of wearable devices to track fertility by assessing physiological changes such as temperature, heart rate, and respiratory rate. +Objective: The primary aims of this study are to review the types of wearables that have been developed and evaluated for menstrual cycle tracking and to examine whether they may detect changes in the menstrual cycle in female individuals. Another aim is to review whether these devices are effective for tracking various stages in the menstrual cycle including ovulation and menstruation. Finally, the secondary aim is to assess whether the studies have validated their findings by reporting accuracy and sensitivity. +Methods: A review of {PubMed} or {MEDLINE} was undertaken to evaluate wearable devices for their effectiveness in predicting fertility and differentiating between the different stages of the menstrual cycle. +Results: Fertility cycle–tracking wearables include devices that can be worn on the wrists, on the fingers, intravaginally, and inside the ear. Wearable devices hold promise for predicting different stages of the menstrual cycle including the fertile window and may be used by female individuals as part of their reproductive health. Most devices had high accuracy for detecting fertility and were able to differentiate between the luteal phase (early and late), fertile window, and menstruation by assessing changes in heart rate, heart rate variability, temperature, and respiratory rate. +Conclusions: More research is needed to evaluate consumer perspectives on reproductive technology for monitoring fertility, and ethical issues around the privacy of digital data need to be addressed. Additionally, there is also a need for more studies to validate and confirm this research, given its scarcity, especially in relation to changes in respiratory rate as a proxy for reproductive cycle staging.}, + pages = {e45139}, + number = {1}, + journaltitle = {Journal of Medical Internet Research}, + author = {Lyzwinski, Lynnette and Elgendi, Mohamed and Menon, Carlo}, + urldate = {2025-02-21}, + date = {2024-02-15}, + note = {Company: Journal of Medical Internet Research +Distributor: Journal of Medical Internet Research +Institution: Journal of Medical Internet Research +Label: Journal of Medical Internet Research +Publisher: {JMIR} Publications Inc., Toronto, Canada}, + file = {Full Text:/home/alex/Zotero/storage/IP23WZLE/Lyzwinski et al. - 2024 - Innovative Approaches to Menstruation and Fertility Tracking Using Wearable Reproductive Health Tech.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/TJN3WV3I/e45139.html:text/html}, +} + +@online{noauthor_zyklus-apps_nodate, + title = {Zyklus-Apps zur Verhütung – sicher oder Gesellschaftsspiel? - {ProQuest}}, + url = {https://www.proquest.com/openview/739071fff0941b30f3a5d33b56259c60/1?pq-origsite=gscholar&cbl=6629261}, + shorttitle = {Zyklus-Apps zur Verhütung – sicher oder Gesellschaftsspiel?}, + abstract = {Explore millions of resources from scholarly journals, books, newspapers, videos and more, on the {ProQuest} Platform.}, + urldate = {2025-02-21}, + langid = {english}, + file = {Snapshot:/home/alex/Zotero/storage/QHYUJU9G/1.html:text/html}, +} + +@article{goeckenjan_continuous_2020, + title = {Continuous Body Temperature Monitoring to Improve the Diagnosis of Female Infertility}, + volume = {80}, + rights = {Georg Thieme Verlag {KG} Stuttgart · New York}, + issn = {0016-5751}, + url = {https://www.thieme-connect.com/products/ejournals/html/10.1055/a-1191-7888}, + doi = {10.1055/a-1191-7888}, + abstract = {Introduction Ovulatory dysfunction is a major cause of female infertility. We evaluated the use of continuous body temperature monitoring with a vaginal biosensor to improve + standard diagnostic procedures for determining ovulatory dysfunction. + +Material and Methods This prospective interventional study was performed in a reproductive medicine department of a university hospital. The menstrual cycles of 51 women with + infertility were monitored and analysed using three different strategies: sonographic and hormonal assessment (standard approach), continuous core body temperature measurement and analysis + using the algorithm of {OvulaRing}, and lowest daily body temperature measurement monitored with a vaginal biosensor and analysed based on the body temperature curves used in natural family + planning. + +Results Statistically significant differences were found in the temperature curves of women with luteal phase deficiency and polycystic ovary syndrome compared to women with normal + menstrual cycles. The analysis of individual cyclofertilograms can be used to detect cycle phases and estimate the date of ovulation. + +Conclusions Continuous body temperature monitoring with a vaginal biosensor can improve the standard diagnostic procedures used to determine ovulatory dysfunction, especially if + dysfunction is due to luteal phase deficiency and polycystic ovary syndrome. Analysis of the lowest daily body temperature combined with the basal body temperature measurements used in + fertility awareness methods may be equieffective to continuous body temperature measurements with {OvulaRing}. The results of this study show that a revised diagnostic approach using fewer + hormonal assessments combined with continuous body temperature monitoring can reduce the number of appointments in an infertility clinic as well as the costs.}, + pages = {702--712}, + journaltitle = {Geburtshilfe und Frauenheilkunde}, + author = {Goeckenjan, Maren and Schiwek, Esther and Wimberger, Pauline}, + urldate = {2025-02-21}, + date = {2020-07-14}, + langid = {english}, + note = {Publisher: Georg Thieme Verlag {KG}}, + keywords = {infertility, Key words + fertility awareness, luteal phase deficiency, polycystic ovary syndrome, vaginal biosensor}, + file = {Full Text PDF:/home/alex/Zotero/storage/QKPIJD23/Goeckenjan et al. - 2020 - Continuous Body Temperature Monitoring to Improve the Diagnosis of Female Infertility.pdf:application/pdf}, +} + +@article{regidor_identification_2018, + title = {Identification and prediction of the fertile window with a new web-based medical device using a vaginal biosensor for measuring the circadian and circamensual core body temperature}, + volume = {34}, + issn = {0951-3590}, + url = {https://doi.org/10.1080/09513590.2017.1390737}, + doi = {10.1080/09513590.2017.1390737}, + abstract = {Fertility awareness-based ({FAB}) methods represent a term that includes all family planning methods that are based on the identification of the fertile window. They are based on the woman’s observation of physiological signs of the fertile and infertile phases of the menstrual cycle. The first approach consists basically in symptothermal methods accompanied by cervical mucus measurements and clinical menstrual cycling data recording. The second most often used methods are the urinary measurement of E3G and luteinizing hormone ({LH}) with a personalized computer system. Hence these systems lack the efficacy of the continuous circadian and circamensual measurement of the core body temperature. Only this approach enables the accurate detection of the ovulation during the fertile window. A new medical device called {OvulaRing} has been developed to fill this gap. In the present study, the system and its first clinical results are presented. {OvulaRing} is a medical device used just like a tampon. The device is a vaginal ring of evatane that contains an integrated biosensor. This sensor measures continuously every 5 min the core body temperature throughout the entire cycle. This device allows a circadian and circamensual intravaginal exact measurement. With this system, 288 measurements are created per day. The system can detect retrospectively and predict prospectively the fertile window of the users. One hundred and fifty eight women aged between 18 and 45 years used this medical device in an open non-randomized clinical study for 15 months. A total of 470 cycles could be recorded and were able for analysis. By the same time in a subgroup of patients, hormonal assessments of {LH}, follicle-stimulating hormone, estradiol and progesterone as well as vaginal ultrasound were performed in parallel between the 9th and the 36th day of the cycle. The validation error due to software errors was 0.89\% for the retrospective analysis; that means that the accuracy for the detection of the ovulation was 99.11\%. Accuracy of 88.8\% for a window of 3 days before ovulation, the day of ovulation and the 3 days after ovulation was achieved for the prospective analysis. In the subgroup of woman with recorded pregnancies, it could be shown that after 3.79 months of use (median) pregnancies were observed. In 67.72\% in up to 3 months, in 16.36\% between 3 and 6 months of use, in 7.27\% between 7 and 9 months, in 5.45\% between 10 and 12 months and in 1.82\% between 13 and 15 months of use of the system. With this new web-based system, a precise determination of the fertile window even in women with ultralong cycles ({\textgreater}35 days) could be detected independently of their personal live circumstances. Exact determination of the fertile window is herewith possible so that {OvulaRing} represents an evolution in the {FAB} method for the cycle diagnosis of women with regular, irregular or anovulatory menstrual cycles.}, + pages = {256--260}, number = {3}, - urldate = {2025-05-27}, - journal = {Fertility and Sterility}, - author = {Garcia, Jairo E. and Seegar Jones, Georgeanna and Wright, George L.}, - month = sep, - year = {1981}, - pages = {308--315}, - file = {PDF:/home/alex/Zotero/storage/DB8PW3QR/Garcia et al. - 1981 - Prediction of the Time of Ovulation.pdf:application/pdf;ScienceDirect Snapshot:/home/alex/Zotero/storage/QLFLZFDJ/S0015028216457304.html:text/html}, + journaltitle = {Gynecological Endocrinology}, + author = {Regidor, Pedro-Antonio and Kaczmarczyk, Marta and Schiweck, Esther and Goeckenjan-Festag, Maren and Alexander, Henry}, + urldate = {2025-02-21}, + date = {2018-03-04}, + pmid = {29082805}, + note = {Publisher: Taylor \& Francis +\_eprint: https://doi.org/10.1080/09513590.2017.1390737}, + keywords = {central nervous system, circadian rhythm, circamensual rhythm, core body temperature, fertile window, Infertility, vagina}, + file = {Full Text PDF:/home/alex/Zotero/storage/ITD68HTW/Regidor et al. - 2018 - Identification and prediction of the fertile window with a new web-based medical device using a vagi.pdf:application/pdf}, } -@article{b_s_novel_2022, - title = {Novel {Technique} for {Confirmation} of the {Day} of {Ovulation} and {Prediction} of {Ovulation} in {Subsequent} {Cycles} {Using} a {Skin}-{Worn} {Sensor} in a {Population} {With} {Ovulatory} {Dysfunction}: {A} {Side}-by-{Side} {Comparison} {With} {Existing} {Basal} {Body} {Temperature} {Algorithm} and {Vaginal} {Core} {Body} {Temperature} {Algorithm}}, +@article{alexander_fertilitatsmonitoring_2014, + title = {Fertilitätsmonitoring mit vaginalem Biosensor ({OvulaRing}©)}, + volume = {74}, + issn = {0016-5751}, + url = {https://www.thieme-connect.com/products/ejournals/abstract/10.1055/s-0034-1388603}, + doi = {10.1055/s-0034-1388603}, + abstract = {Thieme E-Books \& E-Journals}, + pages = {FV\_08\_05}, + journaltitle = {Geburtshilfe und Frauenheilkunde}, + author = {Alexander, H. and Kaczmarczyk, M. and Pretzsch, G. and Kersken, T. and Puschmann, D. and Schiwek, E. and Goeckenjan, M.}, + urldate = {2025-02-21}, + date = {2014-09-05}, + langid = {german}, + keywords = {60. Kongress der Deutschen Gesellschaft für Gynäkologie und Geburtshilfe}, + file = {Snapshot:/home/alex/Zotero/storage/HPL6XYJW/s-0034-1388603.html:text/html}, +} + +@inproceedings{regidor_identifizierung_2018, + title = {Identifizierung und Vorhersage des fertilen Fensters des weiblichen Zyklus mit einem neuen web basierten Medizinprodukt ({OvulaRing}®).}, + volume = {78}, + rights = {Georg Thieme Verlag {KG} Stuttgart · New York}, + url = {https://www.thieme-connect.com/products/ejournals/html/10.1055/s-0038-1671278}, + doi = {10.1055/s-0038-1671278}, + abstract = {Thieme E-Books \& E-Journals}, + pages = {P 23}, + booktitle = {Geburtshilfe und Frauenheilkunde}, + publisher = {Georg Thieme Verlag {KG}}, + author = {Regidor, P. A. and Alexander, H.}, + urldate = {2025-02-21}, + date = {2018-09-20}, + langid = {german}, + note = {{ISSN}: 0016-5751}, + keywords = {Präsidentin der {DGGG} e.V.: Prof. Dr. Birgit Seelbach-Göbel{\textless}/conf-president{\textgreater}{\textless}/conference{\textgreater}}, + file = {Snapshot:/home/alex/Zotero/storage/DW6578ZM/s-0038-1671278.html:text/html}, +} + +@article{sato_novel_2024, + title = {Novel Methodology for Identifying the Occurrence of Ovulation by Estimating Core Body Temperature During Sleeping: Validity and Effectiveness Study}, + volume = {8}, + rights = {Unless stated otherwise, all articles are open-access distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work ("first published in the Journal of Medical Internet Research...") is properly cited with original {URL} and bibliographic citation information. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.}, + url = {https://formative.jmir.org/2024/1/e55834}, + doi = {10.2196/55834}, + shorttitle = {Novel Methodology for Identifying the Occurrence of Ovulation by Estimating Core Body Temperature During Sleeping}, + abstract = {Background: Body temperature is the most-used noninvasive biomarker to determine menstrual cycle and ovulation. However, issues related to its low accuracy are still under discussion. Objective: This study aimed to improve the accuracy of identifying the presence or absence of ovulation within a menstrual cycle. We investigated whether core body temperature ({CBT}) estimation can improve the accuracy of temperature biphasic shift discrimination in the menstrual cycle. The study consisted of 2 parts: experiment 1 assessed the validity of the {CBT} estimation method, while experiment 2 focused on the effectiveness of the method in discriminating biphasic temperature shifts. Methods: In experiment 1, healthy women aged between 18 and 40 years had their true {CBT} measured using an ingestible thermometer and their {CBT} estimated from skin temperature and ambient temperature measured during sleep in both the follicular and luteal phases of their menstrual cycles. This study analyzed the differences between these 2 measurements, the variations in temperature between the 2 phases, and the repeated measures correlation between the true and estimated {CBT}. Experiment 2 followed a similar methodology, but focused on evaluating the diagnostic accuracy of these 2 temperature measurement approaches (estimated {CBT} and traditional oral basal body temperature [{BBT}]) for identifying ovulatory cycles. This was performed using urine luteinizing hormone ({LH}) as the reference standard. Menstrual cycles were categorized based on the results of the {LH} tests, and a temperature shift was identified using a specific criterion called the “three-over-six rule.” This rule and the nested design of the study facilitated the assessment of diagnostic measures, such as sensitivity and specificity. Results: The main findings showed that {CBT} estimated from skin temperature and ambient temperature during sleep was consistently lower than directly measured {CBT} in both the follicular and luteal phases of the menstrual cycle. Despite this, the pattern of temperature variation between these phases was comparable for both the estimated and true {CBT} measurements, suggesting that the estimated {CBT} accurately reflected the cyclical variations in the true {CBT}. Significantly, the {CBT} estimation method showed higher sensitivity and specificity for detecting the occurrence of ovulation than traditional oral {BBT} measurements, highlighting its potential as an effective tool for reproductive health monitoring. The current method for estimating the {CBT} provides a practical and noninvasive method for monitoring {CBT}, which is essential for identifying biphasic shifts in the {BBT} throughout the menstrual cycle. Conclusions: This study demonstrated that the estimated {CBT} derived from skin temperature and ambient temperature during sleep accurately captures variations in true {CBT} and is more accurate in determining the presence or absence of ovulation than traditional oral {BBT} measurements. This method holds promise for improving reproductive health monitoring and understanding of menstrual cycle dynamics.}, + pages = {e55834}, + number = {1}, + journaltitle = {{JMIR} Formative Research}, + author = {Sato, Daisuke and Ikarashi, Koyuki and Nakajima, Fumiko and Fujimoto, Tomomi}, + urldate = {2025-02-20}, + date = {2024-07-05}, + note = {Company: {JMIR} Formative Research +Distributor: {JMIR} Formative Research +Institution: {JMIR} Formative Research +Label: {JMIR} Formative Research +Publisher: {JMIR} Publications Inc., Toronto, Canada}, + file = {PubMed Central Full Text PDF:/home/alex/Zotero/storage/NPBA84BU/Sato et al. - 2024 - Novel Methodology for Identifying the Occurrence of Ovulation by Estimating Core Body Temperature Du.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/BAJUVPFE/e55834.html:text/html}, +} + +@article{royston_identifying_1991, + title = {Identifying the fertile phase of the human menstrual cycle}, volume = {10}, - issn = {2296-4185}, - shorttitle = {Novel {Technique} for {Confirmation} of the {Day} of {Ovulation} and {Prediction} of {Ovulation} in {Subsequent} {Cycles} {Using} a {Skin}-{Worn} {Sensor} in a {Population} {With} {Ovulatory} {Dysfunction}}, - url = {https://www.frontiersin.org/articles/10.3389/fbioe.2022.807139/full}, - doi = {10.3389/fbioe.2022.807139}, - abstract = {Objective: Determine the accuracy of a novel technique for confirmation of the day of ovulation and prediction of ovulation in subsequent cycles for the purpose of conception using a skin-worn sensor in a population with ovulatory dysfunction. -Methods: A total of 80 participants recorded consecutive overnight temperatures using a skin-worn sensor at the same time as a commercially available vaginal sensor for a total of 205 reproductive cycles. The vaginal sensor and its associated algorithm were used to determine the day of ovulation, and the ovulation results obtained using the skin-worn sensor and its associated algorithm were assessed for comparative accuracy alongside a number of other statistical techniques, with a further assessment of the same skin-derived data by means of the “three over six” rule. A number of parameters were used to divide the data into separate comparative groups, and further secondary statistical analyses were performed. -Results: The skin-worn sensor and its associated algorithm (together labeled “SWS”) were 66\% accurate for determining the day of ovulation (±1 day) or the absence of ovulation and 90\% accurate for determining the fertile window (ovulation day ±3 days) in the total study population in comparison to the results obtained from the vaginal sensor and its associated algorithm (together labeled “VS”). -Conclusion: SWS is a useful tool for confirming the fertile window and absence of ovulation (anovulation) in a population with ovulatory dysfunction, both known and Edited by:}, - language = {en}, - urldate = {2025-05-27}, - journal = {Frontiers in Bioengineering and Biotechnology}, - author = {B. S., Hurst and K., Davies and R. C., Milnes and T. G., Knowles and A., Pirrie}, - month = mar, - year = {2022}, - pages = {807139}, - file = {PDF:/home/alex/Zotero/storage/U8UPIT4Q/B. S. et al. - 2022 - Novel Technique for Confirmation of the Day of Ovulation and Prediction of Ovulation in Subsequent C.pdf:application/pdf}, + rights = {Copyright © 1991 John Wiley \& Sons, Ltd.}, + issn = {1097-0258}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.4780100207}, + doi = {10.1002/sim.4780100207}, + abstract = {The identification of the human fertile phase as the time during which a woman or a couple may conceive is elusive. The fertile time depends on many factors in each individual menstrual cycle and may be said to be more of a statistical than a physiological entity. This paper reviews the application of statistical methods to three areas related to conception and the fertile phase. The first is the prediction and detection of ovulation from serial measurements, such as hormones, basal body temperature and cervical mucus, throughout the menstrual cycle. Typically, such variables increase from some baseline level to a peak around ovulation (the most fertile time), then subside to low levels in the postovulatory phase. The statistical challenge is to detect the rise (signalling the onset of potential fertility) and subsequent fall. Analytic methods considered include thresholds, Bayesian change-point models and particularly the cumulative sum (cusum) technique which is both simple to apply and understand, and effective. The second area comprises appropriate methods of analysing and interpreting data from clinical studies of the fertile phase, especially in so-alled natural family planning ({NFP}) where it is usual for women to observe several indices of potential fertility. Such studies usually try to establish the temporal relationships between markers of the fertile phase and examine the success of different combinations of markers in delineating the fertile time in comparison with a standard ‘defined’ phase, for example, the interval from three days before to two days after the peak of luteinizing hormone. The third area is the assessment of the probability of conception on certain days of the cycle, which is vital to the understanding of the fertile phase and its application to {NFP}. Direct estimation of such probabilities is impractical; instead, resort must be made to estimation by maximum likelihood of the parameters of specially constructed models. Suitable models are described. Finally, the need for a new prospective study of the probability of conception in relation to the markers of the fertile phase used in the symptothermal method of {NFP} is discussed.}, + pages = {221--240}, + number = {2}, + journaltitle = {Statistics in Medicine}, + author = {Royston, Patrick}, + urldate = {2025-02-20}, + date = {1991}, + langid = {english}, + file = {Snapshot:/home/alex/Zotero/storage/9XL95LUJ/sim.html:text/html}, } -@article{salles_softed_2024, - title = {{SoftED}: {Metrics} for soft evaluation of time series event detection}, - volume = {198}, - issn = {03608352}, - shorttitle = {{SoftED}}, - url = {https://linkinghub.elsevier.com/retrieve/pii/S0360835224008507}, - doi = {10.1016/j.cie.2024.110728}, - abstract = {Time series event detectors are evaluated mainly by standard classification metrics, focusing solely on detection accuracy. However, inaccuracy in detecting an event can often result from its preceding or delayed effects reflected in neighboring detections. These detections are valuable to trigger necessary actions or help mitigate unwelcome consequences. In this context, current metrics are insufficient and inadequate for the context of event detection. There is a demand for metrics that incorporate both the concept of time and temporal tolerance for neighboring detections. Inspired by fuzzy sets, this paper introduces SoftED metrics, a new set designed for soft evaluating event detectors. They enable the evaluation of the detection accuracy and the degree to which their detections represent events. A new general protocol inspired by competency questions is also introduced to evaluate temporal tolerant metrics for event detection. The SoftED metrics can improve event detection evaluations by associating events and their representative detections, incorporating temporal tolerance in over 36\% of the overall detector evaluations compared to the usual classification metrics. Following the proposed evaluation protocol, SoftED metrics were evaluated by domain specialists who indicated their contribution to detection evaluation and method selection.}, - language = {en}, - urldate = {2025-06-03}, - journal = {Computers \& Industrial Engineering}, - author = {Salles, Rebecca and Lima, Janio and Reis, Michel and Coutinho, Rafaelli and Pacitti, Esther and Masseglia, Florent and Akbarinia, Reza and Chen, Chao and Garibaldi, Jonathan and Porto, Fabio and Ogasawara, Eduardo}, - month = dec, - year = {2024}, - pages = {110728}, - file = {PDF:/home/alex/Zotero/storage/6RYA5HIQ/Salles et al. - 2024 - SoftED Metrics for soft evaluation of time series event detection.pdf:application/pdf}, +@article{su_detection_2017, + title = {Detection of ovulation, a review of currently available methods}, + volume = {2}, + rights = {© 2017 The Authors. Bioengineering \& Translational Medicine is published by Wiley Periodicals, Inc. on behalf of The American Institute of Chemical Engineers}, + issn = {2380-6761}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/btm2.10058}, + doi = {10.1002/btm2.10058}, + abstract = {The ability to identify the precise time of ovulation is important for women who want to plan conception or practice contraception. Here, we review the current literature on various methods for detecting ovulation including a review of point-of-care device technology. We incorporate an examination of methods to detect ovulation that have been developed and practiced for decades and analyze the indications and limitations of each—transvaginal ultrasonography, urinary luteinizing hormone detection, serum progesterone and urinary pregnanediol 3-glucuronide detection, urinary follicular stimulating hormone detection, basal body temperature monitoring, and cervical mucus and salivary ferning analysis. Some point-of-care ovulation detection devices have been developed and commercialized based on these methods, however previous research was limited by small sample size and an inconsistent standard reference to true ovulation.}, + pages = {238--246}, + number = {3}, + journaltitle = {Bioengineering \& Translational Medicine}, + author = {Su, Hsiu-Wei and Yi, Yu-Chiao and Wei, Ting-Yen and Chang, Ting-Chang and Cheng, Chao-Min}, + urldate = {2025-02-20}, + date = {2017}, + langid = {english}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/btm2.10058}, + keywords = {family planning, fertility window, ovulation detection}, + file = {Full Text PDF:/home/alex/Zotero/storage/ZEACCGE5/Su et al. - 2017 - Detection of ovulation, a review of currently available methods.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/RDDQD8EA/btm2.html:text/html}, +} + +@article{owen_physiological_2013, + title = {Physiological Signs of Ovulation and Fertility Readily Observable by Women}, + volume = {80}, + issn = {0024-3639}, + url = {https://doi.org/10.1179/0024363912Z.0000000005}, + doi = {10.1179/0024363912Z.0000000005}, + abstract = {{IntroductionConfirmation} of ovulation can be difficult in clinical practice, as gold standard methods including serial transvaginal ultrasonography, serum luteinizing hormone ({LH}) measurements, or laparoscopic follicular observation are impractical. Numerous surrogate markers have been proposed and evaluated in relation to these gold standards that have more practical clinical applications.{PurposeTo} review the evidence on physiological signs of ovulation timing and fertility in order to determine valid markers that can be easily identified by women.{MethodsA} literature review of primary resources in Ovid Medline was undertaken to identify studies examining physiological signs as they relate to gold standard assessment of ovulation. Studies examining the efficacy/effectiveness of different types of natural family planning were excluded.{ResultsThe} most commonly encountered physiological signs were urine {LH}, cervical mucus, and basal body temperature ({BBT}). Urine {LH} as assessed by home monitoring systems indicated ovulation 91 percent of the time during the 2 days of peak fertility on the monitor and 97 percent during the 2 peak days plus 1. Cervical mucus peak characteristics were identified 78 percent of the time ±1 day, and 91 percent of the time ±2 days of {LH} surge indicating ovulation. Further research supports the importance of cervical mucus in overall fertility, as conception rates were more closely related to mucus quality than to timing of intercourse related to ovulation. As a lone indicator of ovulation, {BBT} is at best a retrospective marker, and functions best in conjunction with other signs of ovulation. Additionally, salivary ferning, salivary and vaginal fluid electrical potential, finger–finger electrical potential, and differential skin temperature were postulated as possible indicators, but were not found to be temporally related to ovulation. The research on differential skin temperature is promising, but minimal thus far in number, and has not been evaluated as an adjunct to {BBT} as yet.{ConclusionHome} urinary {LH} monitors are becoming more widely available and less expensive giving women the potential to assess the ovulatory status of their cycle in real time. Cervical mucus observation is an effective and cost-efficient method, but requires some teaching to increase the confidence of users. In conjunction, {LH} monitors and cervical mucus can give the best indication of fertility and ovulation timing.}, + pages = {17--23}, + number = {1}, + journaltitle = {Linacre Q}, + author = {Owen, Martin}, + urldate = {2025-02-20}, + date = {2013-01-01}, + langid = {english}, + note = {Publisher: {SAGE} Publications Inc}, + file = {Full Text:/home/alex/Zotero/storage/IBVUICCU/Owen - 2013 - Physiological Signs of Ovulation and Fertility Readily Observable by Women.pdf:application/pdf}, +} + +@article{soumpasis_real-life_2020, + title = {Real-life insights on menstrual cycles and ovulation using big data}, + volume = {2020}, + issn = {2399-3529}, + url = {https://doi.org/10.1093/hropen/hoaa011}, + doi = {10.1093/hropen/hoaa011}, + abstract = {What variations underlie the menstrual cycle length and ovulation day of women trying to conceive?Big data from a connected ovulation test revealed the extent of variation in menstrual cycle length and ovulation day in women trying to conceive.Timing intercourse to coincide with the fertile period of a woman maximises the chances of conception. The day of ovulation varies on an inter- and intra-individual level.A total of 32 595 women who had purchased a connected ovulation test system contributed 75 981 cycles for analysis. Day of ovulation was determined from the fertility test results. The connected home ovulation test system enables users to identify their fertile phase. The app benefits users by enabling them to understand their personal fertility information. During each menstrual cycle, users input their perceived cycle length into an accessory application, and data on hormone levels from the tests are uploaded to the application and stored in an anonymised cloud database. This study compared users’ perceived cycle characteristics with actual cycle characteristics. The perceived and actual cycle length information was analysed to provide population ranges.This study analysed data from the at-home use of a commercially available connected home ovulation test by women across the {USA} and {UK}.Overall, 25.3\% of users selected a 28-day cycle as their perceived cycle length; however, only 12.4\% of users actually had a 28-day cycle. Most women (87\%) had actual menstrual cycle lengths between 23 and 35 days, with a normal distribution centred on day 28, and over half of the users (52\%) had cycles that varied by 5 days or more. There was a 10-day spread of observed ovulation days for a 28-day cycle, with the most common day of ovulation being Day 15. Similar variation was observed for all cycle lengths examined. For users who conducted a test on every day requested by the app, a luteinising hormone ({LH}) surge was detected in 97.9\% of cycles.Data were from a self-selected population of women who were prepared to purchase a commercially available product to aid conception and so may not fully represent the wider population. No corresponding demographic data were collected with the cycle information.Using big data has provided more personalised insights into women’s fertility; this could enable women trying to conceive to better time intercourse, increasing the likelihood of conception.The study was funded by {SPD} Development Company Ltd (Bedford, {UK}), a fully owned subsidiary of {SPD} Swiss Precision Diagnostics {GmbH} (Geneva, Switzerland). I.S., B.G. and S.J. are employees of the {SPD} Development Company Ltd.}, + pages = {hoaa011}, + number = {2}, + journaltitle = {Human Reproduction Open}, + author = {Soumpasis, I and Grace, B and Johnson, S}, + urldate = {2025-02-20}, + date = {2020-02-01}, + file = {Full Text PDF:/home/alex/Zotero/storage/PS9UC298/Soumpasis et al. - 2020 - Real-life insights on menstrual cycles and ovulation using big data.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/LTFATQIC/5820371.html:text/html}, +} + +@article{brewis_demographic_2005, + title = {Demographic Evidence That Human Ovulation Is Undetectable (At Least in Pair Bonds)}, + volume = {46}, + issn = {0011-3204}, + url = {https://www.journals.uchicago.edu/doi/abs/10.1086/430016}, + doi = {10.1086/430016}, + pages = {465--471}, + number = {3}, + journaltitle = {Current Anthropology}, + author = {Brewis, Alexandra and Meyer, Mary}, + urldate = {2025-02-20}, + date = {2005-06}, + note = {Publisher: The University of Chicago Press}, +} + +@article{noauthor_monitoring_1987, + title = {Monitoring techniques to predict and detect ovulation}, + volume = {47}, + issn = {0015-0282}, + url = {https://www.sciencedirect.com/science/article/pii/S0015028216500028}, + doi = {10.1016/S0015-0282(16)50002-8}, + abstract = {This study was designed to evaluate the accuracy of various methods in predicting and detecting ovulation in 14 spontaneous and 17 clomiphene citrate …}, + pages = {259--264}, + number = {2}, + journaltitle = {Fertility and Sterility}, + urldate = {2025-02-20}, + date = {1987-02-01}, + langid = {american}, + note = {Publisher: Elsevier}, + file = {PDF:/home/alex/Zotero/storage/RLCZVH5V/1987 - Monitoring techniques to predict and detect ovulation.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/M5P9EZ67/S0015028216500028.html:text/html}, +} + +@article{noauthor_physiological_2016, + title = {Physiological predictors of ovulation and pregnancy risk in a fixed-time artificial insemination program}, + volume = {99}, + issn = {0022-0302}, + url = {https://www.sciencedirect.com/science/article/pii/S0022030216306725}, + doi = {10.3168/jds.2016-11247}, + abstract = {The objective of this study was to determine the relative importance and contribution of several physiological factors as predictors of pregnancy risk…}, + pages = {10077--10092}, + number = {12}, + journaltitle = {Journal of Dairy Science}, + urldate = {2025-02-20}, + date = {2016-12-01}, + langid = {american}, + note = {Publisher: Elsevier}, + file = {Snapshot:/home/alex/Zotero/storage/EBK9WJP4/S0022030216306725.html:text/html}, +} + +@inproceedings{serrano_is_2019-1, + location = {Florence, Italy}, + title = {Is Attention Interpretable?}, + url = {https://aclanthology.org/P19-1282/}, + doi = {10.18653/v1/P19-1282}, + abstract = {Attention mechanisms have recently boosted performance on a range of {NLP} tasks. Because attention layers explicitly weight input components' representations, it is also often assumed that attention can be used to identify information that models found important (e.g., specific contextualized word tokens). We test whether that assumption holds by manipulating attention weights in already-trained text classification models and analyzing the resulting differences in their predictions. While we observe some ways in which higher attention weights correlate with greater impact on model predictions, we also find many ways in which this does not hold, i.e., where gradient-based rankings of attention weights better predict their effects than their magnitudes. We conclude that while attention noisily predicts input components' overall importance to a model, it is by no means a fail-safe indicator.}, + eventtitle = {{ACL} 2019}, + pages = {2931--2951}, + booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, + publisher = {Association for Computational Linguistics}, + author = {Serrano, Sofia and Smith, Noah A.}, + editor = {Korhonen, Anna and Traum, David and Màrquez, Lluís}, + urldate = {2025-02-19}, + date = {2019-07}, + file = {Full Text PDF:/home/alex/Zotero/storage/I6J6YP3C/Serrano and Smith - 2019 - Is Attention Interpretable.pdf:application/pdf}, +} + +@article{noauthor_attention-based_2023, + title = {An attention-based deep learning model for multi-horizon time series forecasting by considering periodic characteristic}, + volume = {185}, + issn = {0360-8352}, + url = {https://www.sciencedirect.com/science/article/abs/pii/S0360835223006915}, + doi = {10.1016/j.cie.2023.109667}, + abstract = {Recently, transformer-based models have exhibited great performance in multi-horizon time series forecasting tasks. However, the core module of these …}, + pages = {109667}, + journaltitle = {Computers \& Industrial Engineering}, + urldate = {2025-02-19}, + date = {2023-11-01}, + langid = {american}, + note = {Publisher: Pergamon}, + file = {Snapshot:/home/alex/Zotero/storage/TJ634T5V/S0360835223006915.html:text/html}, +} + +@article{hu_pattern-oriented_2025, + title = {Pattern-oriented Attention Mechanism for Multivariate Time Series Forecasting}, + volume = {19}, + issn = {1556-4681}, + url = {https://doi.org/10.1145/3712606}, + doi = {10.1145/3712606}, + abstract = {Multivariate time series forecasting is applied in many domains, such as finance, transportation, and industry. The main challenge of precise forecasting lies in accurately capturing latent dependencies. Recent studies develop various frameworks to reduce computational complexity or to enhance the learning of intricate relationships, while lacking interpretability and generality. In this article, we aim to elucidate the capture of dependencies as the recognition of patterns. We believe that patterns can be formally described from two aspects: the shapes of segments that frequently repeat and the corresponding forms of repetitions. Drawing upon this idea, we design a multivariate time series forecasting model named {PRformer},1 which incorporates a pattern-oriented attention mechanism and a pattern-based projector. The attention mechanism can perceive different forms of repetitions by embedded with various similarity evaluation metrics between segments, and filter out noise from segments to extract potential patterns with a statistical-driven weighting scheme. The pattern-based projector is employed to form the forecasting results by deriving the representative patterns from the set of potential ones. By incorporating explicit definitions of patterns, {PRformer} is interpretable and general to various time series scenarios. Experimental results on seven datasets demonstrate that {PRformer} outperforms six state-of-the-art models by about 10.7\% in forecasting accuracy.}, + pages = {38:1--38:26}, + number = {2}, + journaltitle = {{ACM} Trans. Knowl. Discov. Data}, + author = {Hu, Hanwen and Han, Zhangchi and Qian, Shiyou and Yang, Dingyu and Cao, Jian and Xue, Guangtao}, + urldate = {2025-02-19}, + date = {2025-02-06}, +} + +@article{braude_machine_2024, + title = {Machine learning for predicting elective fertility preservation outcomes}, + volume = {14}, + rights = {2024 The Author(s)}, + issn = {2045-2322}, + url = {https://www.nature.com/articles/s41598-024-60671-w}, + doi = {10.1038/s41598-024-60671-w}, + abstract = {This retrospective study applied machine-learning models to predict treatment outcomes of women undergoing elective fertility preservation. Two-hundred-fifty women who underwent elective fertility preservation at a tertiary center, 2019–2022 were included. Primary outcome was the number of metaphase {II} oocytes retrieved. Outcome class was based on oocyte count ({OC}): Low (≤ 8), Medium (9–15) or High (≥ 16). Machine-learning models and statistical regression were used to predict outcome class, first based on pre-treatment parameters, and then using post-treatment data from ovulation-triggering day. {OC} was 136 Low, 80 Medium, and 34 High. Random Forest Classifier ({RFC}) was the most accurate model (pre-treatment receiver operating characteristic ({ROC}) area under the curve ({AUC}) was 77\%, and post-treatment {ROC} {AUC} was 87\%), followed by {XGBoost} Classifier (pre-treatment {ROC} {AUC} 74\%, post-treatment {ROC} {AUC} 86\%). The most important pre-treatment parameters for {RFC} were basal {FSH} (22.6\%), basal {LH} (19.1\%), {AFC} (18.2\%), and basal estradiol (15.6\%). Post-treatment parameters were estradiol levels on trigger-day (17.7\%), basal {FSH} (11\%), basal {LH} (9\%), and {AFC} (8\%). Machine-learning models trained with clinical data appear to predict fertility preservation treatment outcomes with relatively high accuracy.}, + pages = {10158}, + number = {1}, + journaltitle = {Sci Rep}, + author = {Braude, Itai and Haikin Herzberger, Einat and Semo, Mor and Soifer, Kim and Goren Gepstein, Nitzan and Wiser, Amir and Miller, Netanella}, + urldate = {2025-02-11}, + date = {2024-05-02}, + langid = {english}, + note = {Publisher: Nature Publishing Group}, + keywords = {Computational models, Outcomes research}, + file = {Full Text PDF:/home/alex/Zotero/storage/URDGBHLV/Braude et al. - 2024 - Machine learning for predicting elective fertility preservation outcomes.pdf:application/pdf}, +} + +@article{fanton_interpretable_2022, + title = {An interpretable machine learning model for predicting the optimal day of trigger during ovarian stimulation}, + volume = {118}, + issn = {0015-0282, 1556-5653}, + url = {https://www.fertstert.org/article/S0015-0282%2822%2900244-8/fulltext}, + doi = {10.1016/j.fertnstert.2022.04.003}, + pages = {101--108}, + number = {1}, + journaltitle = {Fertility and Sterility}, + author = {Fanton, Michael and Nutting, Veronica and Solano, Funmi and Maeder-York, Paxton and Hariton, Eduardo and Barash, Oleksii and Weckstein, Louis and Sakkas, Denny and Copperman, Alan B. and Loewke, Kevin}, + urldate = {2025-02-11}, + date = {2022-07-01}, + note = {Publisher: Elsevier}, + keywords = {Artificial intelligence, in vitro fertilization, machine learning, ovarian stimulation, trigger}, + file = {Full Text PDF:/home/alex/Zotero/storage/UNX7ASLP/Fanton et al. - 2022 - An interpretable machine learning model for predicting the optimal day of trigger during ovarian sti.pdf:application/pdf}, +} + +@inproceedings{azaria_semi-supervised_2019, + title = {Semi-Supervised Ovulation Detection Based on Multiple Properties}, + url = {https://ieeexplore.ieee.org/document/8995235}, + doi = {10.1109/ICTAI.2019.00039}, + abstract = {Despite being a well-researched problem, ovulation detection in human female remains a difficult task. Most current methods for ovulation detection rely on measurements of a single property (e.g. morning body temperature) or at most on two properties (e.g. both salivary and vaginal electrical resistance). In this paper we present a machine learning based method for detecting the day in which ovulation occurs. Our method considered measurements of five different properties. We crawled a data-set from the web and showed that our method outperforms current state-of-the-art methods for ovulation detection. Our method performs well also when considering measurements of fewer properties. We show that our method's performance can be further improved by using unlabeled data, that is, mensuration cycles without a know ovulation date. Our resulted machine learning model can be very useful for women trying to conceive that have trouble in recognizing their ovulation period, especially when some measurements are missing.}, + eventtitle = {2019 {IEEE} 31st International Conference on Tools with Artificial Intelligence ({ICTAI})}, + pages = {222--228}, + booktitle = {2019 {IEEE} 31st International Conference on Tools with Artificial Intelligence ({ICTAI})}, + author = {Azaria, Amos and Azaria, Seagal}, + urldate = {2025-02-11}, + date = {2019-11}, + note = {{ISSN}: 2375-0197}, + keywords = {ovulation detection, semi supervised learning}, + file = {IEEE Xplore Abstract Record:/home/alex/Zotero/storage/JSAEWRGB/8995235.html:text/html;PDF:/home/alex/Zotero/storage/P74SEG9S/Azaria and Azaria - 2019 - Semi-Supervised Ovulation Detection Based on Multiple Properties.pdf:application/pdf}, +} + +@article{luz_p-656_2023, + title = {P-656 Machine learning algorithm automatically manages and accurately predicts ovulation in natural frozen-thawed embryo transfer cycles.}, + volume = {38}, + issn = {0268-1161}, + url = {https://doi.org/10.1093/humrep/dead093.983}, + doi = {10.1093/humrep/dead093.983}, + abstract = {Can an Artificial Intelligence ({AI}) algorithm automatically manage frozen-thawed embryo transfer ({NC}-{FET}) treatment cycles and give an accurate prediction of ovulation day.An {AI} algorithm automatically managed and predicted the ovulation of {NC}-{FET} treatment cycles with 94.8\% accuracy using an average of 3.01 test days.Today the preferred method for frozen embryo transfer is natural cycle based on ovulation detection. Currently, there is no software capable of managing the treatment cycle automatically and identifying the time of ovulation to support doctor decisions. The aim of this study is to develop a physician support {AI} software for determining ovulation time reliably with high accuracy.2083 {NC}-{FET} cycles from September 2018 to June 2021 were used to develop the ovulation detection and treatment management algorithms.Each cycle had data from at least 2 visits including: hormonal levels (Estrogen/Progesterone/{LH}) and follicle sizes.The dataset was divided into a train set and two test sets. In the 1st test set ovulation was determined by experts’ opinions and the 2nd test set included cycles in which follicle rupture was documented in consecutive ultrasounds.Two algorithms were developed, an ovulation prediction algorithm based on an {NGBoost} model and a treatment management algorithm that used the model to determine if and when to call for a new test or declare the ovulation day.Both algorithms were jointly tuned to reach the highest success rate, defined as providing the correct day of ovulation using the available cycle data, with as few test days as possible.On the first test set, which consisted of 176 cycles in which ovulation was determined through the majority decision of 2 independent experts and the attending physician, the treatment management algorithm required on average 3.01 tests to reach a prediction and successfully predicted the ovulation day in 94.8\% of cycles.In the second test set, which consisted of 29 cycles in which ovulation was determined through the follicular rupture in two consecutive ultrasounds, only the ovulation prediction model was tested. To ensure that the model provides a reliable answer and does not rely solely on the follicular disappearances, examined cycles were tested twice: Once using the ovulation day without the day prior to it, and again using only the day prior to ovulation without the ovulation day itself. The algorithm accurately predicted ovulation in 28 out of 29 instances (96.6\%) using the day of ovulation and in 28 out of 29 instances (96.6\%) using the day before ovulation.The main drawback is this being a retrospective study: while the algorithm was trained to maximize accuracy when it selects the test days, the dataset test days were selected by the attending physicians. Statistical methods were used to overcome this, however further prospective trials are needed to validate the results.This is the first {AI} algorithm designed to automatically manage {NC}-{FET} {IVF} treatment cycles and predict ovulation. The high accuracy and low average tests count might improve treatment outcomes, reduce the patients’ life disruption, and allow physicians to spend less time monitoring their patients’ treatments.not applicable}, + pages = {dead093.983}, + issue = {Supplement\_1}, + journaltitle = {Human Reproduction}, + author = {Luz, A and Hourvitz, R and Reuvenny, S and Youngster, M and Baum, M and Hourvitz, A and Maman, E}, + urldate = {2025-02-11}, + date = {2023-06-01}, + file = {Full Text PDF:/home/alex/Zotero/storage/C8YM765Y/Luz et al. - 2023 - P-656 Machine learning algorithm automatically manages and accurately predicts ovulation in natural.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/WANIZX3H/7202977.html:text/html}, +} + +@article{lin_transformer_2023, + title = {Transformer neural network to predict and interpret pregnancy loss from activity data in Holstein dairy cows}, + volume = {205}, + issn = {0168-1699}, + url = {https://www.sciencedirect.com/science/article/pii/S0168169923000261}, + doi = {10.1016/j.compag.2023.107638}, + abstract = {Predicting/detecting pregnancy loss of dairy cows offers the opportunity to shorten the time interval between artificial inseminations. Although several methods of pregnancy detection are being practiced, models with accurate, timely and interpretable detection of pregnancy are still lacking. This study proposed a transformer neural network to predict the probability of pregnancy loss based on continuous activity data, which were collected from activity-monitoring tags attached to 185 Holstein cows from a commercial dairy farm in Cayuga County, {NY}, {USA}. Our best model achieved an average accuracy of 0.87, F1 score of 0.87, recall of 0.87 and specificity of 0.90 using 14-day time-series activity windows (90\% overlap) using 5-fold cross-validation, outperforming commonly used classic statistical learning and deep learning models for time-series data. The results indicated that our predictive model gave high probabilities of correctly detecting pregnancy loss prior to the increased activities and veterinary confirmation by transrectal ultrasound. In addition, our model interpretation aligned with the changes in the temporal activity levels, revealing that drastic fluctuations in time-series activity data contributed heavily to the final prediction. To the best of our knowledge, this is the first work on developing transformer models for the prediction of pregnancy loss in dairy cows. In addition to facilitating the development of future precision management on modern farms, our work potentiates an increase in the reproductive efficiency and profitability of dairy farms.}, + pages = {107638}, + journaltitle = {Computers and Electronics in Agriculture}, + author = {Lin, Dan and Kenéz, Ákos and {McArt}, Jessica A. A. and Li, Jun}, + urldate = {2025-02-11}, + date = {2023-02-01}, + keywords = {Dairy cow, Precision livestock farming, Pregnancy loss prediction, Time-series activity}, +} + +@article{shkodzik_innovative_2024, + title = {Innovative Approaches to Digital Health in Ovulation Detection: A Review of Current Methods and Emerging Technologies}, + volume = {42}, + issn = {1526-4564}, + doi = {10.1055/s-0044-1793829}, + shorttitle = {Innovative Approaches to Digital Health in Ovulation Detection}, + abstract = {Ovulation is a vital sign, as significant as body temperature, heart rate, respiratory rate, and blood pressure, in assessing overall health and identifying potential health issues. Ovulation is a key event of the menstrual cycle that provides insights into the hormonal and reproductive health aspects. Affected by the orchestra of hormones, namely thyroid, prolactin, and androgens, disruptions in ovulation can indicate endocrinological conditions and lead to gynecological problems, such as heavy menstrual bleeding, irregular periods, amenorrhea, dysmenorrhea, and difficulties in getting pregnant. Monitoring ovulation and detecting disruptions can aid in the early detection of health issues, extending beyond reproductive health concerns. It can help identify underlying causes of symptoms like excessive fatigue and abnormal hair growth. The integration of digital health technologies, such as mobile apps using machine learning algorithms, wearables tracking temperature, heart rate, breath rate, and sleep patterns, and devices measuring reproductive hormones in urine or saliva samples, offers a wealth of opportunities in family planning, early health issue diagnosis, treatment adjustment, and tracking menstrual cycles during assisted reproductive techniques. These advancements provide a comprehensive approach to health monitoring, addressing both reproductive and overall health concerns.}, + pages = {81--89}, + number = {2}, + journaltitle = {Semin Reprod Med}, + author = {Shkodzik, Katerina}, + date = {2024-06}, + pmid = {39572028}, + keywords = {Digital Health, Female, Humans, Mobile Applications, Ovulation, Ovulation Detection, Telemedicine, Wearable Electronic Devices}, +} + +@article{luz_improved_2024, + title = {Improved clinical pregnancy rates in natural frozen-thawed embryo transfer cycles with machine learning ovulation prediction: insights from a retrospective cohort study}, + volume = {14}, + rights = {2024 The Author(s)}, + issn = {2045-2322}, + url = {https://www.nature.com/articles/s41598-024-80356-8}, + doi = {10.1038/s41598-024-80356-8}, + shorttitle = {Improved clinical pregnancy rates in natural frozen-thawed embryo transfer cycles with machine learning ovulation prediction}, + abstract = {This study aims to develop physician support software for determining ovulation time and assess its impact on pregnancy outcomes in natural cycle frozen embryo transfers ({NC}-{FET}). To develop, assess, and validate an ovulation prediction model, three datasets were used: {REI} Ovulation Determination dataset (500 cycles) split into training (309), validation (90), and test (101) sets; the Documented Ovulation dataset (101 cycles) with confirmed ovulation (documented follicular rupture and {LH} surge); and the Clinical Pregnancy Rates dataset (515 {NC}-{FET} cycles), categorized into “Matched” and “Mismatched” based on alignment with the model’s ovulation determination. Pregnancy outcomes were compared between the groups. The ovulation prediction model exhibited 93.85\% and 92.89\% matching rates with the {REI} Ovulation Determination and Documented Ovulation datasets, respectively. In the Clinical Pregnancy Rates dataset, the Matched group (282 cycles) showed significantly higher clinical pregnancy rates than the Mismatched group (34.6\% vs. 25.9\%, p = 0.04) and similar results for patients under 37 (41.1\% vs. 30.7\%, p = 0.04). Logistic regression indicated lower pregnancy rates in Mismatched cases (odds ratio 0.67 for the general population, 0.63 for patients under 37). In conclusion, we introduce a highly accurate {AI} ovulation prediction model. Treatment cycles aligning with the model’s recommendations had significantly increased clinical pregnancy rates.}, + pages = {29451}, + number = {1}, + journaltitle = {Sci Rep}, + author = {Luz, Almog and Hourvitz, Ariel and Moran, Eden and Itzhak, Nevo and Reuvenny, Shachar and Hourvitz, Rohi and Youngster, Michal and Baum, Micha and Maman, Ettie}, + urldate = {2025-02-11}, + date = {2024-11-27}, + langid = {english}, + note = {Publisher: Nature Publishing Group}, + keywords = {Infertility, Outcomes research}, + file = {Full Text PDF:/home/alex/Zotero/storage/BSHNTIFD/Luz et al. - 2024 - Improved clinical pregnancy rates in natural frozen-thawed embryo transfer cycles with machine learn.pdf:application/pdf}, +} + +@article{yu_tracking_2022, + title = {Tracking of menstrual cycles and prediction of the fertile window via measurements of basal body temperature and heart rate as well as machine-learning algorithms}, + volume = {20}, + issn = {1477-7827}, + url = {https://doi.org/10.1186/s12958-022-00993-4}, + doi = {10.1186/s12958-022-00993-4}, + abstract = {Fertility awareness and menses prediction are important for improving fecundability and health management. Previous studies have used physiological parameters, such as basal body temperature ({BBT}) and heart rate ({HR}), to predict the fertile window and menses. However, their accuracy is far from satisfactory. Additionally, few researchers have examined irregular menstruators. Thus, we aimed to develop fertile window and menstruation prediction algorithms for both regular and irregular menstruators.}, + pages = {118}, + number = {1}, + journaltitle = {Reproductive Biology and Endocrinology}, + author = {Yu, Jia-Le and Su, Yun-Fei and Zhang, Chen and Jin, Li and Lin, Xian-Hua and Chen, Lu-Ting and Huang, He-Feng and Wu, Yan-Ting}, + urldate = {2025-02-11}, + date = {2022-08-13}, + keywords = {Basal body temperature, Fertile window, Heart rate, Machine learning, Menstrual cycle, Wearable device}, + file = {Full Text PDF:/home/alex/Zotero/storage/7Z8P97UF/Yu et al. - 2022 - Tracking of menstrual cycles and prediction of the fertile window via measurements of basal body tem.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/SMRBT6EG/s12958-022-00993-4.html:text/html}, +} + +@article{luo_prediction_2025, + title = {Prediction of the fertile window and menstrual cycles with a wearable device via machine-learning algorithms}, + issn = {1472-6483}, + url = {https://www.sciencedirect.com/science/article/pii/S1472648325000021}, + doi = {10.1016/j.rbmo.2025.104795}, + abstract = {Research question +We aimed to develop fertile window and menstruation prediction algorithms through machine learning based on women's physiological parameters data collected by Huawei Band 6 pro from both regular and irregular menstruators. +Design +This was a prospective observational cohort study conducted at Obstetrics and Gynecology Hospital of Fudan University. Participants were recruited from November 2021 to September 2022. Each participant wore Huawei Band 6 pro to record wrist skin temperature ({WST}), heart rate ({HR}), heart rate variability, and respiratory rate. Algorithms were developed to predict the fertile window and menstrual cycle based on {WST} and {HR}. +Results +We included data from 270 and 84 qualified cycles with confirmed ovulations from 136 regular and 47 irregular menstruators. For regular menstruators, the prediction algorithm based on {WST} and {HR} for the fertile window had an accuracy of 85.47\%, a sensitivity of 70.07\%, a specificity of 89.77\%, and {AUC} of 0.869. The algorithms for menstrual first day labelling and onset within 3 days gained an accuracy of 83.6\% and 75.0\%. For irregular menstruators, the accuracy, sensitivity, specificity and {AUC} were 79.85\%, 42.79\%, 87.28\%, and 0.763 respectively, for fertile window prediction. The accuracy of menses labelling and prediction were 61.2\%, and 50.8\% respectively. +Conclusions +Based on {WST} and {HR} data from the wearable device, the algorithms demonstrated reliable performance in predicting the fertile window and menstruation day among regular menstruators. These algorithms also showed potential for assisting irregular menstruators in managing their cycles and planning for conception.}, + pages = {104795}, + journaltitle = {Reproductive {BioMedicine} Online}, + author = {Luo, Chuan and Su, Yun-Fei and Ren, Yun-Yun and Zhang, Qin and Li, Ran and Zhang, Qi and Li, Cheng and Hao, Yan-Hui and Zhang, An-Qi and Zhang, Hao and Huang, He-Feng and Wu, Yan-Ting}, + urldate = {2025-02-11}, + date = {2025-01-07}, + keywords = {Fertile window, Machine learning, Menstrual cycle, Natural cycle, Non-invasive wearable device, Wrist skin temperature}, + file = {PDF:/home/alex/Zotero/storage/YIV6T8MS/Luo et al. - 2025 - Prediction of the fertile window and menstrual cycles with a wearable device via machine-learning al.pdf:application/pdf;ScienceDirect Snapshot:/home/alex/Zotero/storage/3IJMUH82/S1472648325000021.html:text/html}, +} + +@article{maman_prediction_2023, + title = {Prediction of ovulation: new insight into an old challenge}, + volume = {13}, + issn = {2045-2322}, + url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10651856/}, + doi = {10.1038/s41598-023-47241-2}, + shorttitle = {Prediction of ovulation}, + abstract = {Ultrasound monitoring and hormonal blood testing are considered by many as an accurate method to predict ovulation time. However, uniform and validated algorithms for predicting ovulation have yet to be defined. Daily hormonal tests and transvaginal ultrasounds were recorded to develop an algorithm for ovulation prediction. The rupture of the leading ovarian follicle was a marker for ovulation day. The model was validated retrospectively on natural cycles frozen embryo transfer cycles with documented ovulation. Circulating levels of {LH} or its relative variation failed, by themselves, to reliably predict ovulation. Any decrease in estrogen was 100\% associated with ovulation emergence the same day or the next day. Progesterone levels {\textgreater} 2 nmol/L had low specificity to predict ovulation the next day (62.7\%), yet its sensitivity was high (91.5\%). A model for ovulation prediction, combining the three hormone levels and ultrasound was created with an accuracy of 95\% to 100\% depending on the combination of the hormone levels. Model validation showed correct ovulation prediction in 97\% of these cycles. We present an accurate ovulation prediction algorithm. The algorithm is simple and user-friendly so both reproductive endocrinologists and general practitioners can use it to benefit their patients.}, + pages = {20003}, + journaltitle = {Sci Rep}, + author = {Maman, Ettie and Adashi, Eli Y. and Baum, Micha and Hourvitz, Ariel}, + urldate = {2025-02-11}, + date = {2023-11-15}, + pmid = {37968377}, + pmcid = {PMC10651856}, + file = {PubMed Central Full Text PDF:/home/alex/Zotero/storage/MTHDPZ5B/Maman et al. - 2023 - Prediction of ovulation new insight into an old challenge.pdf:application/pdf}, +} + +@article{masuda_machine_2025, + title = {Machine learning model for menstrual cycle phase classification and ovulation day detection based on sleeping heart rate under free-living conditions}, + volume = {187}, + issn = {0010-4825}, + url = {https://www.sciencedirect.com/science/article/pii/S0010482525000551}, + doi = {10.1016/j.compbiomed.2025.109705}, + abstract = {The accurate classification of menstrual cycle phases and detection of ovulation is critical for women's health management, particularly in addressing infertility, alleviating premenstrual syndrome, and preventing hormone-related disorders. However, traditional basal body temperature ({BBT}) measurement methods are susceptible to disruptions in sleep timing and environmental conditions, limiting practical application. This study is aimed to overcome these limitations by introducing a novel feature, heart rate at the circadian rhythm nadir ({minHR}), for classifying menstrual cycle phases and predicting ovulation. A machine learning model was developed using {XGBoost}, and data were collected under free-living conditions from 40 healthy women (18–34 years) over a maximum of three menstrual cycles. Three feature combinations— “day,” “day + {minHR},” and “day + {BBT}”—were evaluated, and model performance was assessed using nested leave-one-group-out cross-validation. The feature “day” represents the number of days elapsed since the onset of menstruation. Participants were stratified into groups depending on high variability and low variability in sleep timing. Results demonstrated that adding {minHR} significantly improved luteal phase classification and ovulation day detection performance compared to “day” only. Furthermore, in participants with high variability in sleep timing, the {minHR}-based model outperformed the {BBT}-based model, significantly improving luteal phase recall and reducing ovulation day detection absolute errors by 2 d (p {\textless} 0.05). These findings highlight the robustness and practicality of the {minHR}-based model for menstrual cycle tracking, particularly in individuals with high variability in sleep timing. The proposed model holds great promise for personalized health management and large-scale epidemiological research.}, + pages = {109705}, + journaltitle = {Computers in Biology and Medicine}, + author = {Masuda, Hazuki and Okada, Shima and Shiozawa, Naruhiro and Sakaue, Yusuke and Manno, Masanobu and Makikawa, Masaaki and Isaka, Tadao}, + urldate = {2025-02-11}, + date = {2025-03-01}, + keywords = {Heart rate, Machine learning, Circadian rhythm, Menstrual cycle tracking, Ovulation day detection, Wearable sensor, {XGBoost}}, + file = {PDF:/home/alex/Zotero/storage/87H6TB3Q/Masuda et al. - 2025 - Machine learning model for menstrual cycle phase classification and ovulation day detection based on.pdf:application/pdf;ScienceDirect Snapshot:/home/alex/Zotero/storage/PC6FSQIA/S0010482525000551.html:text/html}, +} + +@online{noauthor_keras_nodate, + title = {Keras: Deep Learning for humans}, + url = {https://keras.io/}, + urldate = {2024-10-23}, + file = {Keras\: Deep Learning for humans:/home/alex/Zotero/storage/MS4QLPRC/keras.io.html:text/html}, +} + +@article{coninck_dianne_2018, + title = {{DIANNE}: a modular framework for designing, training and deploying deep neural networks on heterogeneous distributed infrastructure}, + volume = {141}, + issn = {01641212}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0164121218300487}, + doi = {10.1016/j.jss.2018.03.032}, + shorttitle = {{DIANNE}}, + pages = {52--65}, + journaltitle = {Journal of Systems and Software}, + author = {Coninck, Elias De and Bohez, Steven and Leroux, Sam and Verbelen, Tim and Vankeirsbilck, Bert and Simoens, Pieter and Dhoedt, Bart}, + urldate = {2024-10-23}, + date = {2018-07}, + langid = {english}, + file = {Full Text:/home/alex/Zotero/storage/SFH656QN/Coninck et al. - 2018 - DIANNE a modular framework for designing, training and deploying deep neural networks on heterogene.pdf:application/pdf}, +} + +@misc{shi_time-moe_2024, + title = {Time-{MoE}: Billion-Scale Time Series Foundation Models with Mixture of Experts}, + url = {http://arxiv.org/abs/2409.16040}, + shorttitle = {Time-{MoE}}, + abstract = {Deep learning for time series forecasting has seen significant advancements over the past decades. However, despite the success of large-scale pre-training in language and vision domains, pre-trained time series models remain limited in scale and operate at a high cost, hindering the development of larger capable forecasting models in real-world applications. In response, we introduce Time-{MoE}, a scalable and unified architecture designed to pre-train larger, more capable forecasting foundation models while reducing inference costs. By leveraging a sparse mixture-of-experts ({MoE}) design, Time-{MoE} enhances computational efficiency by activating only a subset of networks for each prediction, reducing computational load while maintaining high model capacity. This allows Time-{MoE} to scale effectively without a corresponding increase in inference costs. Time-{MoE} comprises a family of decoder-only transformer models that operate in an auto-regressive manner and support flexible forecasting horizons with varying input context lengths. We pre-trained these models on our newly introduced large-scale data Time-300B, which spans over 9 domains and encompassing over 300 billion time points. For the first time, we scaled a time series foundation model up to 2.4 billion parameters, achieving significantly improved forecasting precision. Our results validate the applicability of scaling laws for training tokens and model size in the context of time series forecasting. Compared to dense models with the same number of activated parameters or equivalent computation budgets, our models consistently outperform them by large margin. These advancements position Time-{MoE} as a state-of-the-art solution for tackling real-world time series forecasting challenges with superior capability, efficiency, and flexibility.}, + number = {{arXiv}:2409.16040}, + publisher = {{arXiv}}, + author = {Shi, Xiaoming and Wang, Shiyu and Nie, Yuqi and Li, Dianqi and Ye, Zhou and Wen, Qingsong and Jin, Ming}, + urldate = {2024-10-16}, + date = {2024-10-02}, + eprinttype = {arxiv}, + eprint = {2409.16040}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, + file = {Preprint PDF:/home/alex/Zotero/storage/49N63CMZ/Shi et al. - 2024 - Time-MoE Billion-Scale Time Series Foundation Models with Mixture of Experts.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/HPHN7WNJ/2409.html:text/html}, +} + +@online{noauthor_decoder-only_nodate, + title = {A decoder-only foundation model for time-series forecasting}, + url = {http://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/}, + abstract = {Posted by Rajat Sen and Yichen Zhou, Google Research Time-series forecasting is ubiquitous in various domains, such as retail, finance, manufacturi...}, + urldate = {2024-10-16}, + langid = {english}, + file = {Snapshot:/home/alex/Zotero/storage/JV9JIF73/a-decoder-only-foundation-model-for-time-series-forecasting.html:text/html}, +} + +@misc{goswami_moment_2024, + title = {{MOMENT}: A Family of Open Time-series Foundation Models}, + url = {http://arxiv.org/abs/2402.03885}, + shorttitle = {{MOMENT}}, + abstract = {We introduce {MOMENT}, a family of open-source foundation models for general-purpose time series analysis. Pre-training large models on time series data is challenging due to (1) the absence of a large and cohesive public time series repository, and (2) diverse time series characteristics which make multi-dataset training onerous. Additionally, (3) experimental benchmarks to evaluate these models, especially in scenarios with limited resources, time, and supervision, are still in their nascent stages. To address these challenges, we compile a large and diverse collection of public time series, called the Time series Pile, and systematically tackle time series-specific challenges to unlock large-scale multi-dataset pre-training. Finally, we build on recent work to design a benchmark to evaluate time series foundation models on diverse tasks and datasets in limited supervision settings. Experiments on this benchmark demonstrate the effectiveness of our pre-trained models with minimal data and task-specific fine-tuning. Finally, we present several interesting empirical observations about large pre-trained time series models. Pre-trained models ({AutonLab}/{MOMENT}-1-large) and Time Series Pile ({AutonLab}/Timeseries-{PILE}) are available on Huggingface.}, + number = {{arXiv}:2402.03885}, + publisher = {{arXiv}}, + author = {Goswami, Mononito and Szafer, Konrad and Choudhry, Arjun and Cai, Yifu and Li, Shuo and Dubrawski, Artur}, + urldate = {2024-10-16}, + date = {2024-10-10}, + eprinttype = {arxiv}, + eprint = {2402.03885}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, + file = {Preprint PDF:/home/alex/Zotero/storage/QF6E6J8W/Goswami et al. - 2024 - MOMENT A Family of Open Time-series Foundation Models.pdf:application/pdf}, +} + +@misc{liang_foundation_2024, + title = {Foundation Models for Time Series Analysis: A Tutorial and Survey}, + url = {http://arxiv.org/abs/2403.14735}, + shorttitle = {Foundation Models for Time Series Analysis}, + abstract = {Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications. Recent advances in Foundation Models ({FMs}) have fundamentally reshaped the paradigm of model design for time series analysis, boosting various downstream tasks in practice. These innovative approaches often leverage pre-trained or fine-tuned {FMs} to harness generalized knowledge tailored for time series analysis. This survey aims to furnish a comprehensive and up-to-date overview of {FMs} for time series analysis. While prior surveys have predominantly focused on either application or pipeline aspects of {FMs} in time series analysis, they have often lacked an in-depth understanding of the underlying mechanisms that elucidate why and how {FMs} benefit time series analysis. To address this gap, our survey adopts a methodology-centric classification, delineating various pivotal elements of time-series {FMs}, including model architectures, pre-training techniques, adaptation methods, and data modalities. Overall, this survey serves to consolidate the latest advancements in {FMs} pertinent to time series analysis, accentuating their theoretical underpinnings, recent strides in development, and avenues for future exploration.}, + number = {{arXiv}:2403.14735}, + publisher = {{arXiv}}, + author = {Liang, Yuxuan and Wen, Haomin and Nie, Yuqi and Jiang, Yushan and Jin, Ming and Song, Dongjin and Pan, Shirui and Wen, Qingsong}, + urldate = {2024-10-16}, + date = {2024-06-18}, + eprinttype = {arxiv}, + eprint = {2403.14735}, + keywords = {Computer Science - Machine Learning}, + file = {Preprint PDF:/home/alex/Zotero/storage/YEA28FMJ/Liang et al. - 2024 - Foundation Models for Time Series Analysis A Tutorial and Survey.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/9SGH3AIN/2403.html:text/html}, +} + +@online{noauthor_pytorch-forecasting_2024, + title = {Pytorch-Forecasting}, + url = {https://pytorch-forecasting.readthedocs.io/en/stable/}, + urldate = {2024-10-15}, + date = {2024}, +} + +@misc{taylor_forecasting_2017, + title = {Forecasting at scale}, + rights = {http://creativecommons.org/licenses/by/4.0/}, + url = {https://peerj.com/preprints/3190v2}, + doi = {10.7287/peerj.preprints.3190v2}, + abstract = {Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and high quality forecasts –especially when there are a variety of time series and analysts with expertise in time series modeling are relatively rare. To address these challenges, we describe a practical approach to forecasting “at scale” that combines configurable models with analyst-in-the-loop performance analysis. We propose a modular regression model with interpretable parameters that can be intuitively adjusted by analysts with domain knowledge about the time series. We describe performance analyses to compare and evaluate forecasting procedures, and automatically flag forecasts for manual review and adjustment. Tools that help analysts to use their expertise most effectively enable reliable, practical forecasting of business time series.}, + publisher = {{PeerJ} Preprints}, + author = {Taylor, Sean J and Letham, Benjamin}, + urldate = {2024-10-14}, + date = {2017-09-27}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/GK5AIG2V/Taylor and Letham - 2017 - Forecasting at scale.pdf:application/pdf}, +} + +@collection{hutter_machine_2021, + location = {Cham}, + title = {Machine Learning and Knowledge Discovery in Databases: European Conference, {ECML} {PKDD} 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part {III}}, + volume = {12459}, + rights = {https://www.springernature.com/gp/researchers/text-and-data-mining}, + isbn = {978-3-030-67663-6 978-3-030-67664-3}, + url = {https://link.springer.com/10.1007/978-3-030-67664-3}, + series = {Lecture Notes in Computer Science}, + shorttitle = {Machine Learning and Knowledge Discovery in Databases}, + publisher = {Springer International Publishing}, + editor = {Hutter, Frank and Kersting, Kristian and Lijffijt, Jefrey and Valera, Isabel}, + urldate = {2024-10-14}, + date = {2021}, + langid = {english}, + doi = {10.1007/978-3-030-67664-3}, + file = {Submitted Version:/home/alex/Zotero/storage/JMVJMLJ5/Hutter et al. - 2021 - Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2020, Ghent, B.pdf:application/pdf}, +} + +@incollection{hutter_general_2021, + location = {Cham}, + title = {A General Machine Learning Framework for Survival Analysis}, + volume = {12459}, + isbn = {978-3-030-67663-6 978-3-030-67664-3}, + url = {https://link.springer.com/10.1007/978-3-030-67664-3_10}, + pages = {158--173}, + booktitle = {Machine Learning and Knowledge Discovery in Databases}, + publisher = {Springer International Publishing}, + author = {Bender, Andreas and Rügamer, David and Scheipl, Fabian and Bischl, Bernd}, + editor = {Hutter, Frank and Kersting, Kristian and Lijffijt, Jefrey and Valera, Isabel}, + urldate = {2024-10-14}, + date = {2021}, + langid = {english}, + doi = {10.1007/978-3-030-67664-3_10}, + note = {Series Title: Lecture Notes in Computer Science}, + file = {Submitted Version:/home/alex/Zotero/storage/WQIHZ7IP/Bender et al. - 2021 - A General Machine Learning Framework for Survival Analysis.pdf:application/pdf}, +} + +@online{lightningai_pytorch_2024, + title = {{PyTorch} Lightning}, + url = {https://www.pytorchlightning.ai}, + author = {lightning.ai}, + urldate = {2024-10-14}, + date = {2024}, +} + +@misc{alexandrov_gluonts_2019, + title = {{GluonTS}: Probabilistic Time Series Models in Python}, + url = {http://arxiv.org/abs/1906.05264}, + shorttitle = {{GluonTS}}, + abstract = {We introduce Gluon Time Series ({GluonTS}, available at https://gluon-ts.mxnet.io), a library for deep-learning-based time series modeling. {GluonTS} simplifies the development of and experimentation with time series models for common tasks such as forecasting or anomaly detection. It provides all necessary components and tools that scientists need for quickly building new models, for efficiently running and analyzing experiments and for evaluating model accuracy.}, + number = {{arXiv}:1906.05264}, + publisher = {{arXiv}}, + author = {Alexandrov, Alexander and Benidis, Konstantinos and Bohlke-Schneider, Michael and Flunkert, Valentin and Gasthaus, Jan and Januschowski, Tim and Maddix, Danielle C. and Rangapuram, Syama and Salinas, David and Schulz, Jasper and Stella, Lorenzo and Türkmen, Ali Caner and Wang, Yuyang}, + urldate = {2024-10-14}, + date = {2019-06-14}, + eprinttype = {arxiv}, + eprint = {1906.05264}, + keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, + file = {Preprint PDF:/home/alex/Zotero/storage/JP9K74A8/Alexandrov et al. - 2019 - GluonTS Probabilistic Time Series Models in Python.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/RJYSBT29/1906.html:text/html}, +} + +@misc{cho_learning_2014, + title = {Learning Phrase Representations using {RNN} Encoder-Decoder for Statistical Machine Translation}, + url = {http://arxiv.org/abs/1406.1078}, + abstract = {In this paper, we propose a novel neural network model called {RNN} Encoder-Decoder that consists of two recurrent neural networks ({RNN}). One {RNN} encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the {RNN} Encoder-Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.}, + number = {{arXiv}:1406.1078}, + publisher = {{arXiv}}, + author = {Cho, Kyunghyun and Merrienboer, Bart van and Gulcehre, Caglar and Bahdanau, Dzmitry and Bougares, Fethi and Schwenk, Holger and Bengio, Yoshua}, + urldate = {2024-10-10}, + date = {2014-09-03}, + eprinttype = {arxiv}, + eprint = {1406.1078}, + keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Neural and Evolutionary Computing, Computer Science - Computation and Language}, + file = {Preprint PDF:/home/alex/Zotero/storage/E8WMK2IN/Cho et al. - 2014 - Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/6PTCL8LW/1406.html:text/html}, } @article{hochreiter_long_1997-1, - title = {Long {Short}-{Term} {Memory}}, + title = {Long Short-Term Memory}, volume = {9}, issn = {0899-7667, 1530-888X}, url = {https://direct.mit.edu/neco/article/9/8/1735-1780/6109}, doi = {10.1162/neco.1997.9.8.1735}, - abstract = {Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.}, - language = {en}, - number = {8}, - urldate = {2025-06-11}, - journal = {Neural Computation}, - author = {Hochreiter, Sepp and Schmidhuber, Jürgen}, - month = nov, - year = {1997}, + abstract = {Learningtostoreinformationoverextendedtimeintervalsviarecurrentbackpropagation takesaverylongtime,mostlyduetoinsu cient,decayingerrorbackow.Webrieyreview Hochreiter's1991analysisofthisproblem,thenaddressitbyintroducinganovel,e cient, gradient-basedmethodcalled{\textbackslash}{LongShort}-{TermMemory}"({LSTM}).Truncatingthegradient wherethisdoesnotdoharm,{LSTMcanlearntobridgeminimaltimelagsinexcessof}1000 discretetimestepsbyenforcingconstanterrorowthrough{\textbackslash}constanterrorcarrousels"within specialunits.Multiplicativegateunitslearntoopenandcloseaccesstotheconstanterror ow.{LSTMislocalinspaceandtime};itscomputationalcomplexitypertimestepandweight {isO}(1).Ourexperimentswitharticialdatainvolvelocal,distributed,real-valued,andnoisy patternrepresentations.{IncomparisonswithRTRL},{BPTT},{RecurrentCascade}-Correlation, Elmannets,{andNeuralSequenceChunking},{LSTMleadstomanymoresuccessfulruns},and learnsmuchfaster.{LSTMalsosolvescomplex},articiallongtimelagtasksthathavenever beensolvedbypreviousrecurrentnetworkalgorithms.}, pages = {1735--1780}, - file = {PDF:/home/alex/Zotero/storage/5IE5G9KY/Hochreiter and Schmidhuber - 1997 - Long Short-Term Memory.pdf:application/pdf}, -} - -@book{medsker_recurrent_1999, - title = {Recurrent {Neural} {Networks}: {Design} and {Applications}}, - isbn = {978-1-4200-4917-6}, - shorttitle = {Recurrent {Neural} {Networks}}, - abstract = {With existent uses ranging from motion detection to music synthesis to financial forecasting, recurrent neural networks have generated widespread attention. The tremendous interest in these networks drives Recurrent Neural Networks: Design and Applications, a summary of the design, applications, current research, and challenges of this subfield of artificial neural networks.This overview incorporates every aspect of recurrent neural networks. It outlines the wide variety of complex learning techniques and associated research projects. Each chapter addresses architectures, from fully connected to partially connected, including recurrent multilayer feedforward. It presents problems involving trajectories, control systems, and robotics, as well as RNN use in chaotic systems. The authors also share their expert knowledge of ideas for alternate designs and advances in theoretical aspects.The dynamical behavior of recurrent neural networks is useful for solving problems in science, engineering, and business. This approach will yield huge advances in the coming years. Recurrent Neural Networks illuminates the opportunities and provides you with a broad view of the current events in this rich field.}, - language = {en}, - publisher = {CRC Press}, - author = {Medsker, Larry and Jain, Lakhmi C.}, - month = dec, - year = {1999}, - note = {Google-Books-ID: ME1SAkN0PyMC}, - keywords = {Computers / Computer Engineering, Computers / General, Computers / Software Development \& Engineering / Systems Analysis \& Design, Technology \& Engineering / Electronics / General}, -} - -@article{hochreiter_vanishing_1998, - title = {The {Vanishing} {Gradient} {Problem} {During} {Learning} {Recurrent} {Neural} {Nets} and {Problem} {Solutions}}, - volume = {06}, - issn = {0218-4885, 1793-6411}, - url = {https://www.worldscientific.com/doi/abs/10.1142/S0218488598000094}, - doi = {10.1142/S0218488598000094}, - abstract = {Recurrent nets are in principle capable to store past inputs to produce the currently desired output. Because of this property recurrent nets are used in time series prediction and process control. Practical applications involve temporal dependencies spanning many time steps, e.g. between relevant inputs and desired outputs. In this case, however, gradient based learning methods take too much time. The extremely increased learning time arises because the error vanishes as it gets propagated back. In this article the de-caying error flow is theoretically analyzed. Then methods trying to overcome vanishing gradients are briefly discussed. Finally, experiments comparing conventional algorithms and alternative methods are presented. With advanced methods long time lag problems can be solved in reasonable time.}, - language = {en}, - number = {02}, - urldate = {2025-06-11}, - journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems}, - author = {Hochreiter, Sepp}, - month = apr, - year = {1998}, - pages = {107--116}, -} - -@misc{fdeloche_english_2017, - title = {English: {A} diagram for a one-unit recurrent neural network ({RNN}). {From} bottom to top : input state, hidden state, output state. {U}, {V}, {W} are the weights of the network. {Compressed} diagram on the left and the unfold version of it on the right.}, - shorttitle = {English}, - url = {https://commons.wikimedia.org/wiki/File:Recurrent_neural_network_unfold.svg}, - urldate = {2025-06-11}, - author = {{fdeloche}}, - month = jun, - year = {2017}, - file = {Wikimedia Snapshot:/home/alex/Zotero/storage/RU2XSRZN/FileRecurrent_neural_network_unfold.html:text/html}, -} - -@misc{chevalier_english_2018, - title = {English: {Schematic} of the {Long}-{Short} {Term} {Memory} cell, a component of recurrent neural networks}, - shorttitle = {English}, - url = {https://commons.wikimedia.org/wiki/File:LSTM_Cell.svg}, - urldate = {2025-06-11}, - author = {Chevalier, Guillaume}, - month = may, - year = {2018}, - file = {Wikimedia Snapshot:/home/alex/Zotero/storage/NMYA4ZA3/FileLSTM_Cell.html:text/html}, -} - -@article{twenge_declines_2017, - title = {Declines in {Sexual} {Frequency} among {American} {Adults}, 1989–2014}, - volume = {46}, - issn = {0004-0002, 1573-2800}, - url = {http://link.springer.com/10.1007/s10508-017-0953-1}, - doi = {10.1007/s10508-017-0953-1}, - language = {en}, number = {8}, - urldate = {2025-06-18}, - journal = {Archives of Sexual Behavior}, - author = {Twenge, Jean M. and Sherman, Ryne A. and Wells, Brooke E.}, - month = nov, - year = {2017}, - pages = {2389--2401}, - file = {PDF:/home/alex/Zotero/storage/7FLDCF3U/Twenge et al. - 2017 - Declines in Sexual Frequency among American Adults, 1989–2014.pdf:application/pdf}, + journaltitle = {Neural Computation}, + author = {Hochreiter, Sepp and Schmidhuber, Jürgen}, + urldate = {2024-10-10}, + date = {1997-11-01}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/CZSV2ASE/Hochreiter and Schmidhuber - 1997 - Long Short-Term Memory.pdf:application/pdf}, } -@misc{noauthor_ringpng_nodate, - title = {ring.png (800×800)}, - url = {https://ovularing.com/wp-content/uploads/2021/07/ring.png}, - urldate = {2025-06-25}, +@misc{lim_temporal_2020, + title = {Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting}, + url = {http://arxiv.org/abs/1912.09363}, + abstract = {Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i.e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed historically -- without any prior information on how they interact with the target. While several deep learning models have been proposed for multi-step prediction, they typically comprise black-box models which do not account for the full range of inputs present in common scenarios. In this paper, we introduce the Temporal Fusion Transformer ({TFT}) -- a novel attention-based architecture which combines high-performance multi-horizon forecasting with interpretable insights into temporal dynamics. To learn temporal relationships at different scales, the {TFT} utilizes recurrent layers for local processing and interpretable self-attention layers for learning long-term dependencies. The {TFT} also uses specialized components for the judicious selection of relevant features and a series of gating layers to suppress unnecessary components, enabling high performance in a wide range of regimes. On a variety of real-world datasets, we demonstrate significant performance improvements over existing benchmarks, and showcase three practical interpretability use-cases of {TFT}.}, + number = {{arXiv}:1912.09363}, + publisher = {{arXiv}}, + author = {Lim, Bryan and Arik, Sercan O. and Loeff, Nicolas and Pfister, Tomas}, + urldate = {2024-10-10}, + date = {2020-09-27}, + eprinttype = {arxiv}, + eprint = {1912.09363}, + keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, + file = {Preprint PDF:/home/alex/Zotero/storage/2R2H34KB/Lim et al. - 2020 - Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/ETDAYW36/1912.html:text/html}, } -@misc{noauthor_ovularing_nodate, - title = {{OvulaRing} {Startseite}}, - url = {https://ovularing.com/}, - abstract = {Erfahre hier mehr zu OvulaRing Startseite}, - language = {de-DE}, - urldate = {2025-06-25}, - journal = {OvulaRing}, - file = {Snapshot:/home/alex/Zotero/storage/PD5DBIS4/ovularing.com.html:text/html}, +@misc{nie_time_2023, + title = {A Time Series is Worth 64 Words: Long-term Forecasting with Transformers}, + url = {http://arxiv.org/abs/2211.14730}, + shorttitle = {A Time Series is Worth 64 Words}, + abstract = {We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components: (i) segmentation of time series into subseries-level patches which are served as input tokens to Transformer; (ii) channel-independence where each channel contains a single univariate time series that shares the same embedding and Transformer weights across all the series. Patching design naturally has three-fold benefit: local semantic information is retained in the embedding; computation and memory usage of the attention maps are quadratically reduced given the same look-back window; and the model can attend longer history. Our channel-independent patch time series Transformer ({PatchTST}) can improve the long-term forecasting accuracy significantly when compared with that of {SOTA} Transformer-based models. We also apply our model to self-supervised pre-training tasks and attain excellent fine-tuning performance, which outperforms supervised training on large datasets. Transferring of masked pre-trained representation on one dataset to others also produces {SOTA} forecasting accuracy. Code is available at: https://github.com/yuqinie98/{PatchTST}.}, + number = {{arXiv}:2211.14730}, + publisher = {{arXiv}}, + author = {Nie, Yuqi and Nguyen, Nam H. and Sinthong, Phanwadee and Kalagnanam, Jayant}, + urldate = {2024-10-10}, + date = {2023-03-05}, + eprinttype = {arxiv}, + eprint = {2211.14730}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, + file = {Preprint PDF:/home/alex/Zotero/storage/DG4ZJCWV/Nie et al. - 2023 - A Time Series is Worth 64 Words Long-term Forecasting with Transformers.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/H6XGVBY6/2211.html:text/html}, } -@article{wu_deep_nodate, - title = {Deep {Transformer} {Models} for {Time} {Series} {Forecasting}:{The} {Influenza} {Prevalence} {Case}}, - abstract = {In this paper, we present a new approach to time series forecasting. Time series data are prevalent in many scientific and engineering disciplines. Time series forecasting is a crucial task in modeling time series data, and is an important area of machine learning. In this work we developed a novel method that employs Transformer-based machine learning models to forecast time series data. This approach works by leveraging selfattention mechanisms to learn complex patterns and dynamics from time series data. Moreover, it is a generic framework and can be applied to univariate and multivariate time series data, as well as time series embeddings. Using influenzalike illness (ILI) forecasting as a case study, we show that the forecasting results produced by our approach are favorably comparable to the stateof-the-art.}, - language = {en}, - author = {Wu, Neo and Green, Bradley and Ben, Xue and O'Banion, Shawn}, - file = {PDF:/home/alex/Zotero/storage/GHT5UMNX/Wu et al. - Deep Transformer Models for Time Series ForecastingThe Influenza Prevalence Case.pdf:application/pdf}, +@misc{shao_exploring_2023, + title = {Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis}, + url = {http://arxiv.org/abs/2310.06119}, + shorttitle = {Exploring Progress in Multivariate Time Series Forecasting}, + abstract = {Multivariate Time Series ({MTS}) widely exists in real-word complex systems, such as traffic and energy systems, making their forecasting crucial for understanding and influencing these systems. Recently, deep learning-based approaches have gained much popularity for effectively modeling temporal and spatial dependencies in {MTS}, specifically in Long-term Time Series Forecasting ({LTSF}) and Spatial-Temporal Forecasting ({STF}). However, the fair benchmarking issue and the choice of technical approaches have been hotly debated in related work. Such controversies significantly hinder our understanding of progress in this field. Thus, this paper aims to address these controversies to present insights into advancements achieved. To resolve benchmarking issues, we introduce {BasicTS}, a benchmark designed for fair comparisons in {MTS} forecasting. {BasicTS} establishes a unified training pipeline and reasonable evaluation settings, enabling an unbiased evaluation of over 30 popular {MTS} forecasting models on more than 18 datasets. Furthermore, we highlight the heterogeneity among {MTS} datasets and classify them based on temporal and spatial characteristics. We further prove that neglecting heterogeneity is the primary reason for generating controversies in technical approaches. Moreover, based on the proposed {BasicTS} and rich heterogeneous {MTS} datasets, we conduct an exhaustive and reproducible performance and efficiency comparison of popular models, providing insights for researchers in selecting and designing {MTS} forecasting models.}, + number = {{arXiv}:2310.06119}, + publisher = {{arXiv}}, + author = {Shao, Zezhi and Wang, Fei and Xu, Yongjun and Wei, Wei and Yu, Chengqing and Zhang, Zhao and Yao, Di and Jin, Guangyin and Cao, Xin and Cong, Gao and Jensen, Christian S. and Cheng, Xueqi}, + urldate = {2024-10-10}, + date = {2023-10-09}, + eprinttype = {arxiv}, + eprint = {2310.06119}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, + file = {Preprint PDF:/home/alex/Zotero/storage/7EFZ5IT6/Shao et al. - 2023 - Exploring Progress in Multivariate Time Series Forecasting Comprehensive Benchmarking and Heterogen.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/W6RNWBLM/2310.html:text/html}, } -@article{vaswani_attention_nodate, - title = {Attention {Is} {All} {You} {Need}}, - abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.}, - language = {en}, - author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, Łukasz and Polosukhin, Illia}, - file = {PDF:/home/alex/Zotero/storage/TES5P5PX/Vaswani et al. - Attention Is All You Need.pdf:application/pdf}, -} - -@article{alliende_cervicovaginal_2005, - title = {Cervicovaginal fluid changes to detect ovulation accurately}, - volume = {193}, - copyright = {https://www.elsevier.com/tdm/userlicense/1.0/}, - issn = {00029378}, - url = {https://linkinghub.elsevier.com/retrieve/pii/S0002937804018770}, - doi = {10.1016/j.ajog.2004.11.006}, - abstract = {Objective: The purpose of this study was to evaluate changes in cervicovaginal fluid characteristics to identify ovulation. Study design: Several ovulation indicators were studied in a university-based natural family planning center. Fifteen parous women during 29 ovulatory cycles detected cervicovaginal fluid at the vulva. They self-aspirated their upper vaginal fluid, described it, and kept it for later checking. They also took basal body temperature, collected timed first morning urine samples for estrone and pregnanediol glucuronide enzyme immunoassays, and submitted to serial ovarian transvaginal ultrasound scans. -Results: Considering a G 1-day period since ultrasound ovulation detection or allowing an extra day (ÿ1 to C2), women perceived ovulation from cervicovaginal fluid at the vulva in 76\% or 97\% of cycles, on the basis of their visual description of vaginally extracted fluid in 76\% or 90\%, which rose to 90\% or 97\% for the instructor’s description, and in 76\% or 86\% with a rapid drop in glucuronide ratio. Basal body temperature was less precise (71\% or 79\%). -Conclusion: Evaluation of cervicovaginal fluid changes is an accurate ovulation indicator. Ó 2005 Elsevier Inc. All rights reserved.}, - language = {en}, - number = {1}, - urldate = {2025-07-01}, - journal = {American Journal of Obstetrics and Gynecology}, - author = {Alliende, María Elena and Cabezón, Carlos and Figueroa, Horacio and Kottmann, Cristián}, - month = jul, - year = {2005}, - pages = {71--75}, - file = {PDF:/home/alex/Zotero/storage/NN29EZ6G/Alliende et al. - 2005 - Cervicovaginal fluid changes to detect ovulation accurately.pdf:application/pdf}, -} - -@article{luo_detection_2020, - title = {Detection and {Prediction} of {Ovulation} {From} {Body} {Temperature} {Measured} by an {In}-{Ear} {Wearable} {Thermometer}}, - volume = {67}, - copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html}, - issn = {0018-9294, 1558-2531}, - url = {https://ieeexplore.ieee.org/document/8715448/}, - doi = {10.1109/TBME.2019.2916823}, - abstract = {Objective: We present a non-invasive wearable device for fertility monitoring and propose an effective and flexible statistical learning algorithm to detect and predict ovulation using data captured by this device. Methods: The system consists of an earpiece, which measures the ear canal temperature every 5 minutes during night sleep hours, and a base station that transmits data to a smartphone application for analysis. We establish a data-cleaning protocol for data preprocessing and then fit a Hidden Markov Model (HMM) with two hidden states of high and low temperature to identify the more probable state of each time point via the predicted probabilities. Finally, a post-processing procedure is developed to incorporate biorhythm information to form a time-course biphasic profile for each subject. Results: The performance of the proposed algorithms applied to data collected by the device are compared with traditional methods in terms of match rate with self-reported ovulation days confirmed with an Ovulation Test Kit. Empirical study results from a group of 34 users yielded significant improvements over the traditional methods in terms of detection accuracy (with sensitivity 92.31\%) and prediction power (23.0731.55\% higher). Conclusion: We demonstrated the feasibility for reliable ovulation detection and prediction with high-frequency temperature data collected by a non-invasive wearable device. Significance: Traditional fertility monitoring methods are often either inaccurate or inconvenient. The wearable device and learning algorithm presented in this paper provides a userfriendly and reliable platform for tracking ovulation, which may have a broad impact on both fertility research and real-world family planning.}, - language = {en}, - number = {2}, - urldate = {2025-07-01}, - journal = {IEEE Transactions on Biomedical Engineering}, - author = {Luo, Lan and She, Xichen and Cao, Jiexuan and Zhang, Yunlong and Li, Yijiang and Song, Peter X. K.}, - month = feb, - year = {2020}, - pages = {512--522}, - file = {PDF:/home/alex/Zotero/storage/YLIR4YNX/Luo et al. - 2020 - Detection and Prediction of Ovulation From Body Temperature Measured by an In-Ear Wearable Thermomet.pdf:application/pdf}, -} - -@article{bauman_basal_1981, - title = {Basal {Body} {Temperature}: {Unreliable} {Method} of {Ovulation} {Detection}}, - volume = {36}, - issn = {00150282}, - shorttitle = {Basal {Body} {Temperature}}, - url = {https://linkinghub.elsevier.com/retrieve/pii/S0015028216459169}, - doi = {10.1016/S0015-0282(16)45916-9}, - language = {en}, - number = {6}, - urldate = {2025-07-01}, - journal = {Fertility and Sterility}, - author = {Bauman, Joan E.}, - month = dec, - year = {1981}, - pages = {729--733}, - file = {PDF:/home/alex/Zotero/storage/9T7V8ACT/Bauman - 1981 - Basal Body Temperature Unreliable Method of Ovulation Detection.pdf:application/pdf}, -} - -@misc{noauthor_pytorch_nodate, - title = {{PyTorch}}, - url = {https://pytorch.org/}, - abstract = {PyTorch Foundation is the deep learning community home for the open source PyTorch framework and ecosystem.}, - language = {en-US}, - urldate = {2025-07-11}, - journal = {PyTorch}, - file = {Snapshot:/home/alex/Zotero/storage/K7CTLE77/pytorch.org.html:text/html}, -} - -@article{li_menstrual_2023, - title = {Menstrual cycle length variation by demographic characteristics from the {Apple} {Women}’s {Health} {Study}}, - volume = {6}, - issn = {2398-6352}, - url = {https://www.nature.com/articles/s41746-023-00848-1}, - doi = {10.1038/s41746-023-00848-1}, - abstract = {Abstract - - Menstrual characteristics are important signs of women’s health. Here we examine the variation of menstrual cycle length by age, ethnicity, and body weight using 165,668 cycles from 12,608 participants in the US using mobile menstrual tracking apps. After adjusting for all covariates, mean menstrual cycle length is shorter with older age across all age groups until age 50 and then became longer for those age 50 and older. Menstrual cycles are on average 1.6 (95\%CI: 1.2, 2.0) days longer for Asian and 0.7 (95\%CI: 0.4, 1.0) days longer for Hispanic participants compared to white non-Hispanic participants. Participants with BMI ≥ 40 kg/m - 2 - have 1.5 (95\%CI: 1.2, 1.8) days longer cycles compared to those with BMI between 18.5 and 25 kg/m - 2 - . Cycle variability is the lowest among participants aged 35–39 but are considerably higher by 46\% (95\%CI: 43\%, 48\%) and 45\% (95\%CI: 41\%, 49\%) among those aged under 20 and between 45–49. Cycle variability increase by 200\% (95\%CI: 191\%, 210\%) among those aged above 50 compared to those in the 35–39 age group. Compared to white participants, those who are Asian and Hispanic have larger cycle variability. Participants with obesity also have higher cycle variability. Here we confirm previous observations of changes in menstrual cycle pattern with age across reproductive life span and report new evidence on the differences of menstrual variation by ethnicity and obesity status. Future studies should explore the underlying determinants of the variation in menstrual characteristics.}, - language = {en}, - number = {1}, - urldate = {2025-07-04}, - journal = {npj Digital Medicine}, - author = {Li, Huichu and Gibson, Elizabeth A. and Jukic, Anne Marie Z. and Baird, Donna D. and Wilcox, Allen J. and Curry, Christine L. and Fischer-Colbrie, Tyler and Onnela, Jukka-Pekka and Williams, Michelle A. and Hauser, Russ and Coull, Brent A. and Mahalingaiah, Shruthi}, - month = may, - year = {2023}, - pages = {100}, - file = {PDF:/home/alex/Zotero/storage/9J5N6YIW/Li et al. - 2023 - Menstrual cycle length variation by demographic characteristics from the Apple Women’s Health Study.pdf:application/pdf}, -} - -@article{ecochard_menstrual_2024, - title = {The menstrual cycle is influenced by weekly and lunar rhythms}, - volume = {121}, - issn = {00150282}, - url = {https://linkinghub.elsevier.com/retrieve/pii/S0015028223020769}, - doi = {10.1016/j.fertnstert.2023.12.009}, - abstract = {Objective: To study whether the menstrual cycle has a circaseptan (7 days) rhythm and whether it is associated with the lunar cycle (also defined as the synodic month, it is the cycle of the phases of the Moon as seen from Earth, averaging 29.5 days in length). Design: Cross-sectional study. Subjects: A total of 35,940 European and North American women aged 18–40 years. Exposure: Data were collected in real-life conditions. Intervention: No intervention was performed. Main Outcome Measure: The onset of menstruation was assessed in prospectively measured menstrual cycles (311,064 cycles) over 3 full years (2019–2021). Associations were calculated between the onset of menstruation and the day of the week, and between the onset of menstruation and the lunar phase. -Results: In this large data set, a circaseptan (7-day) rhythmicity of menstruation was observed, with a peak (acrophase) of menstrual onset on Thursdays and Fridays. This circaseptan rhythm was observed in every age group, in every phase of the lunar cycle, and in all seasons. This feature was most pronounced for cycle durations between 27 and 29 days. In winter, the circaseptan rhythm was found in cycles of 27–29 days, but not in other cycle lengths. A circalunar rhythm was also statistically significant, but not as clearly defined as the circaseptan rhythm. The peak (acrophase) of the circalunar rhythm of menstrual onset varied according to the season. In addition, there was a small but statistically significant interaction between the circaseptan rhythm and the lunar cycle. -Conclusion: Although relatively small in amplitude, the weekly rhythm of menstruation was statistically significant. Menstruation occurs more often on Thursdays and Fridays than on other days of the week. This is particularly true for women whose cycles last between 27 and 29 days. Circalunar rhythmicity was also statistically significant. However, it is less pronounced than the weekly rhythm. (Fertil SterilÒ 2024;121:651-9. Ó2023 by American Society for Reproductive Medicine.)}, - language = {en}, - number = {4}, - urldate = {2025-07-03}, - journal = {Fertility and Sterility}, - author = {Ecochard, René and Leiva, Rene and Bouchard, Thomas P. and Van Lamsweerde, Agathe and Pearson, Jack T. and Stanford, Joseph B. and Gronfier, Claude}, - month = apr, - year = {2024}, - pages = {651--659}, - file = {PDF:/home/alex/Zotero/storage/T6DNB4M9/Ecochard et al. - 2024 - The menstrual cycle is influenced by weekly and lunar rhythms.pdf:application/pdf}, -} - -@misc{noauthor_kegg_nodate, - title = {kegg® fertility monitor \& kegel ball {\textbar} {Track} {Key} {Fertility} {Metric}}, - url = {https://kegg.tech/}, - abstract = {Plan your pregnancy with confidence ... kegg® is a medical-grade fertility device that gives you accurate and personalized fertility tracking through cervical mucus.}, - language = {en}, - urldate = {2025-07-02}, - journal = {kegg}, - file = {Snapshot:/home/alex/Zotero/storage/HNFFJPSN/kegg.tech.html:text/html}, -} - -@article{moreno_temporal_1988, - title = {{TEMPORAL} {RELATION} {OF} {OVDLATION} {TO} {SALIVARY} {AND} {VAGINAL} {ELECTRICAL} {RESISTANCE} {PATTERNS}: {IMPLICATIONS} {FOR} {NATURAL} {FAMILY} {PLANNING}}, - abstract = {An independent assessment of the CUETM Monitor (Zetek, Aurora, Colorado) as an ovulation predictor was made with emphasis on its potential role in "natural family planning". The device provides a digital measurement of the electrical resistance of saliva and vaginal secretions. Twenty-nine menstrual cycles from 11 regularly cycling women were monitored with basal temperatures, urinary LH, pelvic ultrasound and the CUE monitor. Patterns of peak salivary electrical resistance were able to predict ovulation on average 5.3 (51.9 SD) days in advance. Despite variations in total length of the follicular phase from cycle to cycle, the within-subject variation of this predictive interval was quite small. Nadirs in the electrical resistance of vaginal secretions occurred within 2 days of ovulation in all but one patient. Variation in this interval from cycle-tocycle was small as well. We propose an algorithm for the use of these intervals in "natural family planning" that could safely reduce the monthly abstinence period of present methods. The simplicity, objectivity and consistency of this device could result in their greater general acceptance.}, - language = {en}, - author = {Moreno, Jorge E and Doody, Michael C and Besch, Paige}, - month = oct, - year = {1988}, - file = {PDF:/home/alex/Zotero/storage/FFW8KBZQ/Moreno et al. - TEMPORAL RELATION OF OVDLATION TO SALIVARY AND VAGINAL ELECTRICAL RESISTANCE PATTERNS IMPLICATIONS.pdf:application/pdf}, -} - -@misc{noauthor_trackle_nodate, - title = {trackle - einfach hormonfrei verhüten}, - url = {https://trackle.de/}, - abstract = {Das trackle Sensorsystem hilft Dir, einfach, sicher und hormonfrei zu verhüten. Jetzt informieren und symptothermale Methode nutzen!}, - language = {de}, - urldate = {2025-07-02}, - journal = {trackle}, - file = {Snapshot:/home/alex/Zotero/storage/KNFY8X2K/trackle.de.html:text/html}, -} - -@article{zhu_accuracy_2021, - title = {The {Accuracy} of {Wrist} {Skin} {Temperature} in {Detecting} {Ovulation} {Compared} to {Basal} {Body} {Temperature}: {Prospective} {Comparative} {Diagnostic} {Accuracy} {Study}}, - volume = {23}, - issn = {1438-8871}, - shorttitle = {The {Accuracy} of {Wrist} {Skin} {Temperature} in {Detecting} {Ovulation} {Compared} to {Basal} {Body} {Temperature}}, - url = {https://www.jmir.org/2021/6/e20710}, - doi = {10.2196/20710}, - abstract = {Background: As a daily point measurement, basal body temperature (BBT) might not be able to capture the temperature shift in the menstrual cycle because a single temperature measurement is present on the sliding scale of the circadian rhythm. Wrist skin temperature measured continuously during sleep has the potential to overcome this limitation. -Objective: This study compares the diagnostic accuracy of these two temperatures for detecting ovulation and to investigate the correlation and agreement between these two temperatures in describing thermal changes in menstrual cycles. -Methods: This prospective study included 193 cycles (170 ovulatory and 23 anovulatory) collected from 57 healthy women. Participants wore a wearable device (Ava Fertility Tracker bracelet 2.0) that continuously measured the wrist skin temperature during sleep. Daily BBT was measured orally and immediately upon waking up using a computerized fertility tracker with a digital thermometer (Lady-Comp). An at-home luteinizing hormone test was used as the reference standard for ovulation. The diagnostic accuracy of using at least one temperature shift detected by the two temperatures in detecting ovulation was evaluated. For ovulatory cycles, repeated measures correlation was used to examine the correlation between the two temperatures, and mixed effect models were used to determine the agreement between the two temperature curves at different menstrual phases. -Results: Wrist skin temperature was more sensitive than BBT (sensitivity 0.62 vs 0.23; P{\textless}.001) and had a higher true-positive rate (54.9\% vs 20.2\%) for detecting ovulation; however, it also had a higher false-positive rate (8.8\% vs 3.6\%), resulting in lower specificity (0.26 vs 0.70; P=.002). The probability that ovulation occurred when at least one temperature shift was detected was 86.2\% for wrist skin temperature and 84.8\% for BBT. Both temperatures had low negative predictive values (8.8\% for wrist skin temperature and 10.9\% for BBT). Significant positive correlation between the two temperatures was only found in the follicular phase (rmcorr correlation coefficient=0.294; P=.001). Both temperatures increased during the postovulatory phase with a greater increase in the wrist skin temperature (range of increase: 0.50 °C vs 0.20 °C). During the menstrual phase, the wrist skin temperature exhibited a greater and more rapid decrease (from 36.13 °C to 35.80 °C) than BBT (from 36.31 °C to 36.27 °C). During the preovulatory phase, there were minimal changes in both temperatures and small variations in the estimated daily difference between the two temperatures, indicating an agreement between the two curves. -Conclusions: For women interested in maximizing the chances of pregnancy, wrist skin temperature continuously measured during sleep is more sensitive than BBT for detecting ovulation. The difference in the diagnostic accuracy of these methods was likely attributed to the greater temperature increase in the postovulatory phase and greater temperature decrease during the menstrual phase for the wrist skin temperatures.}, - language = {en}, - number = {6}, - urldate = {2025-07-02}, - journal = {Journal of Medical Internet Research}, - author = {Zhu, Tracy Y and Rothenbühler, Martina and Hamvas, Györgyi and Hofmann, Anja and Welter, JoEllen and Kahr, Maike and Kimmich, Nina and Shilaih, Mohaned and Leeners, Brigitte}, - month = jun, - year = {2021}, - pages = {e20710}, - file = {PDF:/home/alex/Zotero/storage/PY7HCR3K/Zhu et al. - 2021 - The Accuracy of Wrist Skin Temperature in Detecting Ovulation Compared to Basal Body Temperature Pr.pdf:application/pdf}, -} - -@article{shilaih_modern_2018, - title = {Modern fertility awareness methods: wrist wearables capture the changes in temperature associated with the menstrual cycle}, - volume = {38}, - issn = {0144-8463}, - shorttitle = {Modern fertility awareness methods}, - url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6265623/}, - doi = {10.1042/BSR20171279}, - abstract = {Core and peripheral body temperatures are affected by changes in reproductive hormones during the menstrual cycle. Women worldwide use the basal body temperature (BBT) method to aid and prevent conception. However, prior research suggests that taking one’s daily temperature can prove inconvenient and subject to environmental factors. We investigate whether a more automatic, non-invasive temperature measurement system can detect changes in temperature across the menstrual cycle. We examined how wrist skin temperature (WST), measured with wearable sensors, correlates with urinary tests of ovulation and may serve as a new method of fertility tracking. One hundred and thirty-six eumenorrheic, non-pregnant women participated in an observational study. Participants wore WST biosensors during sleep and reported their daily activities. An at-home luteinizing hormone (LH) test was used to confirm ovulation. WST was recorded across 437 cycles (mean cycles/participant = 3.21, S.D. = 2.25). We tested the relationship between the fertile window and WST temperature shifts, using the BBT three-over-six rule. A sustained 3-day temperature shift was observed in 357/437 cycles (82\%), with the lowest cycle temperature occurring in the fertile window 41\% of the time. Most temporal shifts (307/357, 86\%) occurred on ovulation day (OV) or later. The average early-luteal phase temperature was 0.33°C higher than in the fertile window. Menstrual cycle changes in WST were impervious to lifestyle factors, like having sex, alcohol, or eating prior to bed, that, in prior work, have been shown to obfuscate BBT readings. Although currently costlier than BBT, the present study suggests that WST could be a promising, convenient parameter for future multiparameter fertility awareness methods.}, - number = {6}, - urldate = {2025-07-02}, - journal = {Bioscience Reports}, - author = {Shilaih, Mohaned and Goodale, Brianna M. and Falco, Lisa and Kübler, Florian and De Clerck, Valerie and Leeners, Brigitte}, - month = nov, - year = {2018}, - pmid = {29175999}, - pmcid = {PMC6265623}, - pages = {BSR20171279}, - file = {Full Text PDF:/home/alex/Zotero/storage/CXNSXAFC/Shilaih et al. - 2018 - Modern fertility awareness methods wrist wearables capture the changes in temperature associated wi.pdf:application/pdf}, -} - -@misc{sl_ava_nodate, - title = {Ava {Fertility} {Tracker}}, - url = {https://www.avawomen.com/}, - abstract = {See your 5 best days to conceive in real-time. Go beyond ovulation day, and make use of your full fertile window to increase your chances of pregnancy}, - language = {en}, - urldate = {2025-07-02}, - journal = {AvaWomen}, - author = {S.L, Ava Women}, - file = {Snapshot:/home/alex/Zotero/storage/SMIE7YIR/www.avawomen.com.html:text/html}, -} - -@misc{noauthor_fact_sheet_studie_210621_2025, - title = {fact\_sheet\_studie\_210621}, - shorttitle = {daysy\_fact\_sheet}, - url = {https://dfxyyqidohkoi.cloudfront.net/media/filer_public/ff/86/ff8646d2-8d33-445c-9148-5979b60abbae/fact_sheet_studie_210621.pdf}, - urldate = {2025-07-02}, - month = jul, - year = {2025}, - file = {PDF:/home/alex/Zotero/storage/8VDBFS2G/fact_sheet_studie_210621.pdf:application/pdf}, -} - -@misc{electronics_zykluscomputer_nodate, - title = {Zykluscomputer {Daysy} - 100 \% natürlich und sehr genau!}, - url = {https://de.daysy.me/}, - abstract = {Daysy Zykluscomputer - einfach, hormonfrei \& über 99\% genau. ✓ Medizinprodukt zur Berechnung Deiner fruchtbaren Tage ✓ Natürliche Familienplanung ✓ Erhöhe Deine Lebensqualität!}, - language = {de}, - urldate = {2025-07-02}, - author = {Electronics, Valley}, - file = {Snapshot:/home/alex/Zotero/storage/ZGIZ669S/de.daysy.me.html:text/html}, -} - -@misc{noauthor_natural_nodate, - title = {Natural {Cycles}: {Natural} {Birth} {Control} {\textbar} {No} {Hormones} or {Side} {Effects}}, - shorttitle = {Natural {Cycles}}, - url = {https://www.naturalcycles.com}, - abstract = {Natural Cycles birth control is 93\% effective with typical use and 98\% effective with perfect use. Learn more about hormone-free birth control today.}, - language = {en-US}, - urldate = {2025-07-02}, - journal = {Natural Cycles}, - file = {Snapshot:/home/alex/Zotero/storage/MCUDFIHC/www.naturalcycles.com.html:text/html}, -} - -@article{weiss_confirmation_2022, - title = {Confirmation of human ovulation in assisted reproduction using an adhesive axillary thermometer ({femSense}®)}, - volume = {4}, - issn = {2673-253X}, - url = {https://www.frontiersin.org/articles/10.3389/fdgth.2022.930010/full}, - doi = {10.3389/fdgth.2022.930010}, - abstract = {Objective - Timing for sexual intercourse is important in achieving pregnancy in natural menstrual cycles. Different methods of detecting the fertile window have been invented, among them luteinization hormone (LH) to predict ovulation and biphasic body basal temperature (BBT) to confirm ovulation retrospectively. The gold standard to detect ovulation in gynecology practice remains transvaginal ultrasonography in combination with serum progesterone. In this study we evaluated a wearable temperature sensing patch (femSense®) using continuous body temperature measurement to confirm ovulation and determine the end of the fertile window. - - - Methods - 96 participants received the femSense® system consisting of an adhesive axillary thermometer patch and a smartphone application, where patients were asked to document information about their previous 3 cycles. Based on the participants data, the app predicted the cycle length and the estimated day of ovulation. From these predictions, the most probable fertile window and the day for applying the patch were derived. Participants applied and activated the femSense® patch on the calculated date, from which the patch continuously recorded their body temperature throughout a period of up to 7 days to confirm ovulation. Patients documented their daily urinary LH test positivity, and a transvaginal ultrasound was performed on day cycle day 7, 10, 12 and 14/15 to investigate the growth of one dominant follicle. If a follicle reached 15 mm in diameter, an ultrasound examination was carried out every day consecutively until ovulation. On the day ovulation was detected, serum progesterone was measured to confirm the results of the ultrasound. The performance of femSense® was evaluated by comparing the day of ovulation confirmation with the results of ovulation prediction (LH test) and detection (transvaginal ultrasound). - - - Results - - The femSense® system confirmed ovulation occurrence in 60 cases (81.1\%) compared to 48 predicted cases (64.9\%) with the LH test ( - p -  = 0.041). Subgroup analysis revealed a positive trend for the femSense® system of specific ovulation confirmation within the fertile window of 24 h after ovulation in 42 of 74 cases (56.8\%). Cycle length, therapy method or infertility reason of the patient did not influence accuracy of the femSense® system. - - - - Conclusions - The femSense® system poses a promising alternative to the traditional BBT method and is a valuable surrogate marker to transvaginal ultrasound for confirmation of ovulation.}, - language = {en}, - urldate = {2025-07-02}, - journal = {Frontiers in Digital Health}, - author = {Weiss, Gregor and Strohmayer, Karl and Koele, Werner and Reinschissler, Nina and Schenk, Michael}, - month = sep, - year = {2022}, - pages = {930010}, - file = {PDF:/home/alex/Zotero/storage/C3IU2JVF/Weiss et al. - 2022 - Confirmation of human ovulation in assisted reproduction using an adhesive axillary thermometer (fem.pdf:application/pdf}, -} - -@article{moghissi_accuracy_1976, - title = {Accuracy of {Basal} {Body} {Temperature} for {Ovulation} {Detection}}, - volume = {27}, - issn = {00150282}, - url = {https://linkinghub.elsevier.com/retrieve/pii/S0015028216422570}, - doi = {10.1016/S0015-0282(16)42257-0}, - language = {en}, - number = {12}, - urldate = {2025-07-02}, - journal = {Fertility and Sterility}, - author = {Moghissi, Kamran S.}, - month = dec, - year = {1976}, - pages = {1415--1421}, - file = {PDF:/home/alex/Zotero/storage/GKF98BHR/Moghissi - 1976 - Accuracy of Basal Body Temperature for Ovulation Detection.pdf:application/pdf}, -} - -@article{thigpen_oura_2025, - title = {Oura {Ring} as a {Tool} for {Ovulation} {Detection}: {Validation} {Analysis}}, - volume = {27}, - issn = {1438-8871}, - shorttitle = {Oura {Ring} as a {Tool} for {Ovulation} {Detection}}, - url = {https://www.jmir.org/2025/1/e60667}, - doi = {10.2196/60667}, - abstract = {Background: Oura Ring is a wearable device that estimates ovulation dates using physiology data recorded from the finger. Estimating the ovulation date can aid fertility management for conception or nonhormonal contraception and provides insights into follicular and luteal phase lengths. Across the reproductive lifespan, changes in these phase lengths can serve as a biomarker for reproductive health. -Objective: We assessed the strengths, weaknesses, and limitations of using physiology from the Oura Ring to estimate the ovulation date. We compared performance across cycle length, cycle variability, and participant age. In each subgroup, we compared the algorithm’s performance with the traditional calendar method, which estimates the ovulation date based on an individual’s last period start date and average menstrual cycle length. -Methods: The study sample contained 1155 ovulatory menstrual cycles from 964 participants recruited from the Oura Ring commercial database. Ovulation prediction kits served as a benchmark to evaluate the performance. The Fisher test was used to determine an odds ratio to assess if ovulation detection rate significantly differed between methods or subgroups. The Mann-Whitney U test was used to determine if the accuracy of the estimated ovulation date differed between the estimated and reference ovulation dates. -Results: The physiology method detected 1113 (96.4\%) of 1155 ovulations with an average error of 1.26 days, which was significantly lower (U=904942.0, P{\textless}.001) than the calendar method’s average error of 3.44 days. The physiology method had significantly better accuracy across all cycle lengths, cycle variability groups, and age groups compared with the calendar method (P{\textless}.001). The physiology method detected fewer ovulations in short cycles (odds ratio 3.56, 95\% CI 1.65-8.06; P=.008) but did not differ between typical and long or abnormally long cycles. Abnormally long cycle lengths were associated with decreased accuracy (U=22,383, P=.03), with a mean absolute error of 1.7 (SEM .09) days compared with 1.18 (SEM .02) days. The physiology method was not associated with differences in accuracy across age or typical cycle variability, while the calendar method performed significantly worse in participants with irregular cycles (U=21,643, P{\textless}.001). -Conclusions: The physiology method demonstrated superior accuracy over the calendar method, with approximately 3-fold improvement. Calendar-based fertility tracking could be used as a backup in cases of insufficient physiology data but should be used with caution, particularly for individuals with irregular menstrual cycles. Our analyses suggest the physiology method can reliably estimate ovulation dates for adults aged 18-52 years, across a variety of cycle lengths, and in users with regular or irregular cycles. This method may be used as a tool to improve fertile window estimation, which can aid in conceiving or preventing pregnancies. This method also offers a low-effort solution for follicular and luteal phase length tracking, which are key biomarkers for reproductive health.}, - language = {en}, - urldate = {2025-07-15}, - journal = {Journal of Medical Internet Research}, - author = {Thigpen, Nina and Patel, Shyamal and Zhang, Xi}, - month = jan, - year = {2025}, - pages = {e60667}, - file = {PDF:/home/alex/Zotero/storage/TECKQLN7/Thigpen et al. - 2025 - Oura Ring as a Tool for Ovulation Detection Validation Analysis.pdf:application/pdf}, -} - -@article{guida_efficacy_1999, - title = {Efficacy of methods for determining ovulation in a natural family planning program}, - volume = {72}, - issn = {00150282}, - url = {https://linkinghub.elsevier.com/retrieve/pii/S0015028299003659}, - doi = {10.1016/S0015-0282(99)00365-9}, - abstract = {Objective: To evaluate the efficacy in ovulation detection of methods used in natural family planning in comparison with pelvic ultrasonography. Design: Prospective analysis of ovulation detection by natural family planning methods and ultrasonography. Setting: Natural family planning clinic, Department of Obstetrics and Gynecology, University of Naples “Federico II”. Patient(s): Forty healthy women who were highly motivated to use natural family planning. Intervention(s): None. Main Outcome Measure(s): Transvaginal ultrasonographic findings, urinary LH levels, salivary b-glucuronidase activity, salivary ferning levels and characteristics of cervical mucus, and BBT. -Result(s): Urinary LH level determination yielded a 100\% correlation with the simultaneous ultrasonographic diagnosis of ovulation. Mucus sensations and characteristics yielded a 48.3\% correlation when simultaneously evaluated with ovulation. b-Glucuronidase levels yielded a 27.7\% correlation. The salivary ferning test had a 36.8\% ovulation-detection rate the day of ovulation, but 58.7\% of results were uninterpretable. Body temperature measurements yielded a 30.4\% correlation with the simultaneous ultrasonographic diagnosis of ovulation. -Conclusion(s): Measuring urinary LH levels is an excellent method for determining ovulation. Although variations in mucus characteristics and basal body temperature correlate somewhat with ovulation, the length of the fertile period is overestimated with these methods. The salivary ferning test and measurement of b-glucuronidase levels are not good methods for home ovulation testing. (Fertil Sterilt 1999;72:900 – 4. ©1999 by American Society for Reproductive Medicine.)}, - language = {en}, - number = {5}, - urldate = {2025-07-15}, - journal = {Fertility and Sterility}, - author = {Guida, Maurizio and Tommaselli, Giovanni A and Palomba, Stefano and Pellicano, Massimiliano and Moccia, Gianfranco and Di Carlo, Costantino and Nappi, Carmine}, - month = nov, - year = {1999}, - pages = {900--904}, - file = {PDF:/home/alex/Zotero/storage/UAS9AGTU/Guida et al. - 1999 - Efficacy of methods for determining ovulation in a natural family planning program.pdf:application/pdf}, -} - -@article{wallach_prediction_1980, - title = {Prediction and {Detection} of {Ovulation}}, - volume = {34}, - issn = {00150282}, - url = {https://linkinghub.elsevier.com/retrieve/pii/S0015028216448880}, - doi = {10.1016/S0015-0282(16)44888-0}, - language = {en}, - number = {2}, - urldate = {2025-07-15}, - journal = {Fertility and Sterility}, - author = {Wallach, Edward and Moghissi, Kamran S.}, - month = aug, - year = {1980}, - pages = {89--98}, - file = {PDF:/home/alex/Zotero/storage/JREACGRA/Wallach and Moghissi - 1980 - Prediction and Detection of Ovulation.pdf:application/pdf}, -} - -@misc{pham_dropout_2014, - title = {Dropout improves {Recurrent} {Neural} {Networks} for {Handwriting} {Recognition}}, - url = {http://arxiv.org/abs/1312.4569}, - doi = {10.48550/arXiv.1312.4569}, - abstract = {Recurrent neural networks (RNNs) with Long Short-Term memory cells currently hold the best known results in unconstrained handwriting recognition. We show that their performance can be greatly improved using dropout - a recently proposed regularization method for deep architectures. While previous works showed that dropout gave superior performance in the context of convolutional networks, it had never been applied to RNNs. In our approach, dropout is carefully used in the network so that it does not affect the recurrent connections, hence the power of RNNs in modeling sequence is preserved. Extensive experiments on a broad range of handwritten databases confirm the effectiveness of dropout on deep architectures even when the network mainly consists of recurrent and shared connections.}, - urldate = {2025-07-22}, - publisher = {arXiv}, - author = {Pham, Vu and Bluche, Théodore and Kermorvant, Christopher and Louradour, Jérôme}, - month = mar, - year = {2014}, - note = {arXiv:1312.4569 [cs]}, - keywords = {Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Computer Science - Computer Vision and Pattern Recognition}, - file = {Full Text PDF:/home/alex/Zotero/storage/IA52LNE8/Pham et al. - 2014 - Dropout improves Recurrent Neural Networks for Handwriting Recognition.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/LJ8EJ5PS/1312.html:text/html}, -} - -@article{wen_time_2023, - title = {Time {Series} {Prediction} {Based} on {LSTM}-{Attention}-{LSTM} {Model}}, - volume = {11}, - issn = {2169-3536}, - url = {https://ieeexplore.ieee.org/document/10124729/}, - doi = {10.1109/ACCESS.2023.3276628}, - abstract = {Time series forecasting uses data from the past periods of time to predict future information, which is of great significance in many applications. Existing time series forecasting methods still have problems such as low accuracy when dealing with some non-stationary multivariate time series data forecasting. Aiming at the shortcomings of existing methods, in this paper we propose a new time series forecasting model LSTM-attention-LSTM. The model uses two LSTM models as the encoder and decoder, and introduces an attention mechanism between the encoder and decoder. The model has two distinctive features: first, by using the attention mechanism to calculate the interrelationship between sequence data, it overcomes the disadvantage of the coder-and-decoder model in that the decoder cannot obtain sufficiently long input sequences; second, it is suitable for sequence forecasting with long time steps. In this paper we validate the proposed model based on several real data sets, and the results show that the LSTM-attention-LSTM model is more accurate than some currently dominant models in prediction. The experiment also assessed the effect of the attention mechanism at different time steps by varying the time step.}, - urldate = {2025-07-22}, - journal = {IEEE Access}, - author = {Wen, Xianyun and Li, Weibang}, - year = {2023}, - keywords = {Predictive models, Time series analysis, Time series forecasting, attention mechanisms, Autoregressive processes, Data models, Decoding, encoder and decoder model, Forecasting, Logic gates, long short-term memory networks}, - pages = {48322--48331}, - file = {Full Text PDF:/home/alex/Zotero/storage/3M54PVSE/Wen and Li - 2023 - Time Series Prediction Based on LSTM-Attention-LSTM Model.pdf:application/pdf}, -} - -@misc{goyal_accurate_2018, - title = {Accurate, {Large} {Minibatch} {SGD}: {Training} {ImageNet} in 1 {Hour}}, - shorttitle = {Accurate, {Large} {Minibatch} {SGD}}, - url = {http://arxiv.org/abs/1706.02677}, - doi = {10.48550/arXiv.1706.02677}, - abstract = {Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves {\textasciitilde}90\% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency.}, - urldate = {2025-07-22}, - publisher = {arXiv}, - author = {Goyal, Priya and Dollár, Piotr and Girshick, Ross and Noordhuis, Pieter and Wesolowski, Lukasz and Kyrola, Aapo and Tulloch, Andrew and Jia, Yangqing and He, Kaiming}, - month = apr, - year = {2018}, - note = {arXiv:1706.02677 [cs]}, - keywords = {Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Computer Vision and Pattern Recognition}, - annote = {Comment: Tech report (v2: correct typos)}, - file = {Full Text PDF:/home/alex/Zotero/storage/5MKPLWI7/Goyal et al. - 2018 - Accurate, Large Minibatch SGD Training ImageNet in 1 Hour.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/TRYJFLDW/1706.html:text/html}, -} - -@article{pearl_factors_1933, - title = {{FACTORS} {IN} {HUMAN} {FERTILITY} {AND} {THEIR} {STATISTICAL} {EVALUATION}}, - volume = {222}, - copyright = {https://www.elsevier.com/tdm/userlicense/1.0/}, - issn = {01406736}, - url = {https://linkinghub.elsevier.com/retrieve/pii/S0140673601186484}, - doi = {10.1016/S0140-6736(01)18648-4}, - language = {en}, - number = {5741}, - urldate = {2025-07-30}, - journal = {The Lancet}, - author = {Pearl, Raymond}, - month = sep, - year = {1933}, - pages = {607--611}, -} - -@article{gaskins_predictors_2018, - title = {Predictors of sexual intercourse frequency among couples trying to conceive}, - volume = {15}, - issn = {1743-6095}, - url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882561/}, - doi = {10.1016/j.jsxm.2018.02.005}, - abstract = {Background -Little is known about the predictors of sexual intercourse frequency (SIF) among couples trying to conceive despite the well-established link between SIF and fecundity. - -Aim -To evaluate the male and female demographic, occupational, and lifestyle predictors of SIF among couples. - -Methods -469 couples without a history of infertility participating in the Longitudinal Investigation of Fertility and the Environment Study (2005–2009) were followed for ≤1 year while trying to conceive. At enrollment, both partners were interviewed about demographic, occupational, lifestyle, and psychological characteristics using standardized questionnaires. Multivariable generalized linear mixed models with Poisson distribution was used to estimate the adjusted percent difference in SIF across exposure categories. - -Outcomes -SIF was recorded in daily journals and summarized as average SIF per month. - -Results -The median (interquartile range) SIF during follow-up was 6 (4–9) acts per month. For every year increase in female and male age, SIF decreased by −0.8\% (95\% CI −2.5, 1.0\%) and −1.7\% (95\% CI −3.1, −0.3\%). Women with high school education or less and those of non-White race had 34.4\% and 16.0\% higher SIF, respectively. A similar trend was seen for male education and race. Only couples where both partners (but not just one partner) worked rotating shifts had −39.1\% (95\% CI −61.0, −5.0\%) lower SIF compared to couples where neither partner worked rotating shifts. Male (but not female) exercise was associated with 13.2\% (95\% CI 1.7, 26.0\%) higher SIF. Diagnosis of a mood or anxiety disorder in the male (but not female) was associated with a 26.0\% (95\% CI −42.7, −4.4\%) lower SIF. Household income, smoking status, BMI, night work, alcohol intake, psychosocial stress were not associated with SIF. - -Clinical Implications -Even among couples trying to conceive, there was substantial variation in SIF. Both partners’ age, education, race, and rotating shift work as well as male exercise and mental health play an important role in determining SIF. - -Strengths \& Limitations -As this was a secondary analysis of an existing study, we lacked information on many pertinent psychological and relationship quality variables and the hormonal status of participants, which could have affected SIF. The unique population-based couple design, however, captured both partners’ demographics, occupational characteristics, lifestyle behaviors in advance of their daily, prospective reporting of SIF, which was a major strength. - -Conclusion -Important predictors of SIF among couples attempting to conceive include male exercise and mental health and both partners’ age, education, race, and rotating shift work.}, - number = {4}, - urldate = {2025-07-30}, - journal = {The journal of sexual medicine}, - author = {Gaskins, Audrey J. and Sundaram, Rajeshwari and Buck Louis, Germaine M. and Chavarro, Jorge E.}, - month = apr, - year = {2018}, - pmid = {29523477}, - pmcid = {PMC5882561}, - pages = {519--528}, - file = {Full Text PDF:/home/alex/Zotero/storage/S3JV4TU2/Gaskins et al. - 2018 - Predictors of sexual intercourse frequency among couples trying to conceive.pdf:application/pdf}, -} - -@article{earle_use_2021, - title = {Use of menstruation and fertility app trackers: a scoping review of the evidence}, - volume = {47}, - issn = {2515-1991, 2515-2009}, - shorttitle = {Use of menstruation and fertility app trackers}, - url = {https://jfprhc.bmj.com/lookup/doi/10.1136/bmjsrh-2019-200488}, - doi = {10.1136/bmjsrh-2019-200488}, - abstract = {Introduction There has been a phenomenal worldwide increase in the development and use of mobile health applications (mHealth apps) that monitor menstruation and fertility. Critics argue that many of the apps are inaccurate and lack evidence from either clinical trials or user experience. The aim of this scoping review is to provide an overview of the research literature on mHealth apps that track menstruation and fertility. -Methods This project followed the PRISMA Extension for Scoping Reviews. The ACM, CINAHL, Google Scholar, PubMed and Scopus databases were searched for material published between 1 January 2010 and 30 April 2019. Data summary and synthesis were used to chart and analyse the data. -Results In total 654 records were reviewed. Subsequently, 135 duplicate records and 501 records that did not meet the inclusion criteria were removed. Eighteen records from 13 countries form the basis of this review. The papers reviewed cover a variety of disciplinary and methodological frameworks. Three main themes were identified: fertility and reproductive health tracking, pregnancy planning, and pregnancy prevention. -Conclusions Motivations for fertility app use are varied, overlap and change over time, although women want apps that are accurate and evidence-based regardless of whether they are tracking their fertility, planning a pregnancy or using the app as a form of contraception. There is a lack of critical debate and engagement in the development, evaluation, usage and regulation of fertility and menstruation apps. The paucity of evidence-based research and absence of fertility, health professionals and users in studies is raised.}, - language = {en}, - number = {2}, - urldate = {2025-08-04}, - journal = {BMJ Sexual \& Reproductive Health}, - author = {Earle, Sarah and Marston, Hannah R and Hadley, Robin and Banks, Duncan}, - month = apr, - year = {2021}, - pages = {90--101}, - file = {PDF:/home/alex/Zotero/storage/XMT448BI/Earle et al. - 2021 - Use of menstruation and fertility app trackers a scoping review of the evidence.pdf:application/pdf}, -} - -@article{owen_physiology_nodate, - title = {Physiology of the menstrual cycle}, - abstract = {Modern techniques of bioassay have permitted correlation of hormonal secretion with genital tissue changes during the normal menstrual cycle. During the follicular phase, estrogen secretion rises while other hormone levels are low. At ovulation luteinizing hormone and follicle-stimulating hormone surges are associated with falling estrogen levels. Secretions of progesterone and estrogen again are characteristic of the lutea! phase ending with menstruation. Gonadotrophin-releasing hormones are detectable just before the luteinizing hormone and follicle-stimulating hormone surges. Basal body temperature rises with ovulation and is still the most reliable clinical indicator, although ferning and spinnbarkeit (when present) are also quite helpful. Vaginal smears are probably less useful except in the hands of experienced observers. Am. J. Clin. Nutr. 28: 333-338, 1975.}, - language = {en}, - author = {Owen, A}, - file = {PDF:/home/alex/Zotero/storage/J9ITWN5R/Owen - Physiology of the menstrual cycle.pdf:application/pdf}, -} - -@article{albertson_prediction_1987, - title = {The prediction of ovulation and monitoring of the fertile period}, - volume = {3}, - copyright = {http://www.springer.com/tdm}, - issn = {0267-4874, 1573-7195}, - url = {http://link.springer.com/10.1007/BF01849284}, - doi = {10.1007/BF01849284}, - abstract = {Simple and reliable methods have been sought for both predicting and confirming ovulation. Application of these methods could include management of infertile couples to aid in conception and for increasing the reliability of natural family planning (NFCF) as a method of birth control. With the advent of specific hormone assays, serial measurements of estrogens, progesterone (and metabolites), and luteinizing hormone have been the gold standard of monitoring ovarian function in women. However, newer and simpler methodologies have been described and are currently either in use or being tested. These include the measurement of basal body temperature (BBT), the evaluation of the volume, consistency and electro-conductivity of cervicovaginal fluid, salivary steroid content and cellular enzymatic activity, the use of enzymelinked immunosorbent assays applied to solid-phase formats, and the investigation of new hormonal molecules as markers of reproductive state and function. These new technologies are described herein and their potential for monitoring ovarian function is discussed.}, - language = {en}, - number = {4}, - urldate = {2025-08-01}, - journal = {Advances in Contraception}, - author = {Albertson, B. D. and Zinaman, M. J.}, - month = dec, - year = {1987}, - pages = {263--290}, - file = {PDF:/home/alex/Zotero/storage/SQCGXH4T/Albertson and Zinaman - 1987 - The prediction of ovulation and monitoring of the fertile period.pdf:application/pdf}, +@article{zhang_crossformer_2023, + title = {{CROSSFORMER}: {TRANSFORMER} {UTILIZING} {CROSS}- {DIMENSION} {DEPENDENCY} {FOR} {MULTIVARIATE} {TIME} {SERIES} {FORECASTING}}, + abstract = {Recently many deep models have been proposed for multivariate time series ({MTS}) forecasting. In particular, Transformer-based models have shown great potential because they can capture long-term dependency. However, existing Transformerbased models mainly focus on modeling the temporal dependency (cross-time dependency) yet often omit the dependency among different variables (crossdimension dependency), which is critical for {MTS} forecasting. To fill the gap, we propose Crossformer, a Transformer-based model utilizing cross-dimension dependency for {MTS} forecasting. In Crossformer, the input {MTS} is embedded into a 2D vector array through the Dimension-Segment-Wise ({DSW}) embedding to preserve time and dimension information. Then the Two-Stage Attention ({TSA}) layer is proposed to efficiently capture the cross-time and cross-dimension dependency. Utilizing {DSW} embedding and {TSA} layer, Crossformer establishes a Hierarchical Encoder-Decoder ({HED}) to use the information at different scales for the final forecasting. Extensive experimental results on six real-world datasets show the effectiveness of Crossformer against previous state-of-the-arts.}, + author = {Zhang, Yunhao and Yan, Junchi}, + date = {2023}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/NM9CETJS/Zhang and Yan - 2023 - CROSSFORMER TRANSFORMER UTILIZING CROSS- DIMENSION DEPENDENCY FOR MULTIVARIATE TIME SERIES FORECAST.pdf:application/pdf}, } @article{leader_prediction_1985, title = {The prediction of ovulation: a comparison of the basal body temperature graph, cervical mucus score, and real-time pelvic ultrasonography}, volume = {43}, issn = {00150282}, - shorttitle = {The prediction of ovulation}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0015028216484360}, doi = {10.1016/S0015-0282(16)48436-0}, - language = {en}, - number = {3}, - urldate = {2025-08-01}, - journal = {Fertility and Sterility}, - author = {Leader, Arthur and Wiseman, David and Taylor, Patrick J.}, - month = mar, - year = {1985}, + shorttitle = {The prediction of ovulation}, pages = {385--388}, + number = {3}, + journaltitle = {Fertility and Sterility}, + author = {Leader, Arthur and Wiseman, David and Taylor, Patrick J.}, + urldate = {2025-08-01}, + date = {1985-03}, + langid = {english}, file = {PDF:/home/alex/Zotero/storage/2GQKZ2YH/Leader et al. - 1985 - The prediction of ovulation a comparison of the basal body temperature graph, cervical mucus score,.pdf:application/pdf}, } +@article{albertson_prediction_1987, + title = {The prediction of ovulation and monitoring of the fertile period}, + volume = {3}, + rights = {http://www.springer.com/tdm}, + issn = {0267-4874, 1573-7195}, + url = {http://link.springer.com/10.1007/BF01849284}, + doi = {10.1007/BF01849284}, + abstract = {Simple and reliable methods have been sought for both predicting and confirming ovulation. Application of these methods could include management of infertile couples to aid in conception and for increasing the reliability of natural family planning ({NFCF}) as a method of birth control. With the advent of specific hormone assays, serial measurements of estrogens, progesterone (and metabolites), and luteinizing hormone have been the gold standard of monitoring ovarian function in women. However, newer and simpler methodologies have been described and are currently either in use or being tested. These include the measurement of basal body temperature ({BBT}), the evaluation of the volume, consistency and electro-conductivity of cervicovaginal fluid, salivary steroid content and cellular enzymatic activity, the use of enzymelinked immunosorbent assays applied to solid-phase formats, and the investigation of new hormonal molecules as markers of reproductive state and function. These new technologies are described herein and their potential for monitoring ovarian function is discussed.}, + pages = {263--290}, + number = {4}, + journaltitle = {Adv Contracept}, + author = {Albertson, B. D. and Zinaman, M. J.}, + urldate = {2025-08-01}, + date = {1987-12}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/SQCGXH4T/Albertson and Zinaman - 1987 - The prediction of ovulation and monitoring of the fertile period.pdf:application/pdf}, +} + +@article{owen_physiology_nodate, + title = {Physiology of the menstrual cycle}, + abstract = {Modern techniques of bioassay have permitted correlation of hormonal secretion with genital tissue changes during the normal menstrual cycle. During the follicular phase, estrogen secretion rises while other hormone levels are low. At ovulation luteinizing hormone and follicle-stimulating hormone surges are associated with falling estrogen levels. Secretions of progesterone and estrogen again are characteristic of the lutea! phase ending with menstruation. Gonadotrophin-releasing hormones are detectable just before the luteinizing hormone and follicle-stimulating hormone surges. Basal body temperature rises with ovulation and is still the most reliable clinical indicator, although ferning and spinnbarkeit (when present) are also quite helpful. Vaginal smears are probably less useful except in the hands of experienced observers. Am. J. Clin. Nutr. 28: 333-338, 1975.}, + author = {Owen, A}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/J9ITWN5R/Owen - Physiology of the menstrual cycle.pdf:application/pdf}, +} + +@article{earle_use_2021, + title = {Use of menstruation and fertility app trackers: a scoping review of the evidence}, + volume = {47}, + issn = {2515-1991, 2515-2009}, + url = {https://jfprhc.bmj.com/lookup/doi/10.1136/bmjsrh-2019-200488}, + doi = {10.1136/bmjsrh-2019-200488}, + shorttitle = {Use of menstruation and fertility app trackers}, + abstract = {Introduction There has been a phenomenal worldwide increase in the development and use of mobile health applications ({mHealth} apps) that monitor menstruation and fertility. Critics argue that many of the apps are inaccurate and lack evidence from either clinical trials or user experience. The aim of this scoping review is to provide an overview of the research literature on {mHealth} apps that track menstruation and fertility. +Methods This project followed the {PRISMA} Extension for Scoping Reviews. The {ACM}, {CINAHL}, Google Scholar, {PubMed} and Scopus databases were searched for material published between 1 January 2010 and 30 April 2019. Data summary and synthesis were used to chart and analyse the data. +Results In total 654 records were reviewed. Subsequently, 135 duplicate records and 501 records that did not meet the inclusion criteria were removed. Eighteen records from 13 countries form the basis of this review. The papers reviewed cover a variety of disciplinary and methodological frameworks. Three main themes were identified: fertility and reproductive health tracking, pregnancy planning, and pregnancy prevention. +Conclusions Motivations for fertility app use are varied, overlap and change over time, although women want apps that are accurate and evidence-based regardless of whether they are tracking their fertility, planning a pregnancy or using the app as a form of contraception. There is a lack of critical debate and engagement in the development, evaluation, usage and regulation of fertility and menstruation apps. The paucity of evidence-based research and absence of fertility, health professionals and users in studies is raised.}, + pages = {90--101}, + number = {2}, + journaltitle = {{BMJ} Sex Reprod Health}, + author = {Earle, Sarah and Marston, Hannah R and Hadley, Robin and Banks, Duncan}, + urldate = {2025-08-04}, + date = {2021-04}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/XMT448BI/Earle et al. - 2021 - Use of menstruation and fertility app trackers a scoping review of the evidence.pdf:application/pdf}, +} + +@misc{loshchilov_decoupled_2019, + title = {Decoupled Weight Decay Regularization}, + url = {http://arxiv.org/abs/1711.05101}, + doi = {10.48550/arXiv.1711.05101}, + abstract = {L\$\_2\$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is {\textbackslash}emph\{not\} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L\$\_2\$ regularization (often calling it "weight decay" in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by {\textbackslash}emph\{decoupling\} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard {SGD} and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with {SGD} with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in {TensorFlow} and {PyTorch}; the complete source code for our experiments is available at https://github.com/loshchil/{AdamW}-and-{SGDW}}, + number = {{arXiv}:1711.05101}, + publisher = {{arXiv}}, + author = {Loshchilov, Ilya and Hutter, Frank}, + urldate = {2025-08-05}, + date = {2019-01-04}, + eprinttype = {arxiv}, + eprint = {1711.05101 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Mathematics - Optimization and Control}, + file = {Full Text PDF:/home/alex/Zotero/storage/KQSRMXBI/Loshchilov and Hutter - 2019 - Decoupled Weight Decay Regularization.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/AADJFDL3/1711.html:text/html}, +} + +@misc{smith_disciplined_2018, + title = {A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay}, + url = {http://arxiv.org/abs/1803.09820}, + doi = {10.48550/arXiv.1803.09820}, + shorttitle = {A disciplined approach to neural network hyper-parameters}, + abstract = {Although deep learning has produced dazzling successes for applications of image, speech, and video processing in the past few years, most trainings are with suboptimal hyper-parameters, requiring unnecessarily long training times. Setting the hyper-parameters remains a black art that requires years of experience to acquire. This report proposes several efficient ways to set the hyper-parameters that significantly reduce training time and improves performance. Specifically, this report shows how to examine the training validation/test loss function for subtle clues of underfitting and overfitting and suggests guidelines for moving toward the optimal balance point. Then it discusses how to increase/decrease the learning rate/momentum to speed up training. Our experiments show that it is crucial to balance every manner of regularization for each dataset and architecture. Weight decay is used as a sample regularizer to show how its optimal value is tightly coupled with the learning rates and momentums. Files to help replicate the results reported here are available.}, + number = {{arXiv}:1803.09820}, + publisher = {{arXiv}}, + author = {Smith, Leslie N.}, + urldate = {2025-08-05}, + date = {2018-04-24}, + eprinttype = {arxiv}, + eprint = {1803.09820 [cs]}, + keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing}, + file = {Full Text PDF:/home/alex/Zotero/storage/SWJYFVTG/Smith - 2018 - A disciplined approach to neural network hyper-parameters Part 1 -- learning rate, batch size, mome.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/KWQGJ7MX/1803.html:text/html}, +} + @misc{wang_is_2024, - title = {Is {Mamba} {Effective} for {Time} {Series} {Forecasting}?}, + title = {Is Mamba Effective for Time Series Forecasting?}, url = {http://arxiv.org/abs/2403.11144}, doi = {10.48550/arXiv.2403.11144}, - abstract = {In the realm of time series forecasting (TSF), it is imperative for models to adeptly discern and distill hidden patterns within historical time series data to forecast future states. Transformer-based models exhibit formidable efficacy in TSF, primarily attributed to their advantage in apprehending these patterns. However, the quadratic complexity of the Transformer leads to low computational efficiency and high costs, which somewhat hinders the deployment of the TSF model in real-world scenarios. Recently, Mamba, a selective state space model, has gained traction due to its ability to process dependencies in sequences while maintaining near-linear complexity. For TSF tasks, these characteristics enable Mamba to comprehend hidden patterns as the Transformer and reduce computational overhead compared to the Transformer. Therefore, we propose a Mamba-based model named Simple-Mamba (S-Mamba) for TSF. Specifically, we tokenize the time points of each variate autonomously via a linear layer. A bidirectional Mamba layer is utilized to extract inter-variate correlations and a Feed-Forward Network is set to learn temporal dependencies. Finally, the generation of forecast outcomes through a linear mapping layer. Experiments on thirteen public datasets prove that S-Mamba maintains low computational overhead and achieves leading performance. Furthermore, we conduct extensive experiments to explore Mamba's potential in TSF tasks. Our code is available at https://github.com/wzhwzhwzh0921/S-D-Mamba.}, - urldate = {2025-08-05}, - publisher = {arXiv}, + abstract = {In the realm of time series forecasting ({TSF}), it is imperative for models to adeptly discern and distill hidden patterns within historical time series data to forecast future states. Transformer-based models exhibit formidable efficacy in {TSF}, primarily attributed to their advantage in apprehending these patterns. However, the quadratic complexity of the Transformer leads to low computational efficiency and high costs, which somewhat hinders the deployment of the {TSF} model in real-world scenarios. Recently, Mamba, a selective state space model, has gained traction due to its ability to process dependencies in sequences while maintaining near-linear complexity. For {TSF} tasks, these characteristics enable Mamba to comprehend hidden patterns as the Transformer and reduce computational overhead compared to the Transformer. Therefore, we propose a Mamba-based model named Simple-Mamba (S-Mamba) for {TSF}. Specifically, we tokenize the time points of each variate autonomously via a linear layer. A bidirectional Mamba layer is utilized to extract inter-variate correlations and a Feed-Forward Network is set to learn temporal dependencies. Finally, the generation of forecast outcomes through a linear mapping layer. Experiments on thirteen public datasets prove that S-Mamba maintains low computational overhead and achieves leading performance. Furthermore, we conduct extensive experiments to explore Mamba's potential in {TSF} tasks. Our code is available at https://github.com/wzhwzhwzh0921/S-D-Mamba.}, + number = {{arXiv}:2403.11144}, + publisher = {{arXiv}}, author = {Wang, Zihan and Kong, Fanheng and Feng, Shi and Wang, Ming and Yang, Xiaocui and Zhao, Han and Wang, Daling and Zhang, Yifei}, - month = apr, - year = {2024}, - note = {arXiv:2403.11144 [cs]}, + urldate = {2025-08-05}, + date = {2024-04-27}, + eprinttype = {arxiv}, + eprint = {2403.11144 [cs]}, keywords = {Computer Science - Machine Learning}, file = {Full Text PDF:/home/alex/Zotero/storage/ELY6R6NS/Wang et al. - 2024 - Is Mamba Effective for Time Series Forecasting.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/AR3TC3EH/2403.html:text/html}, } -@misc{smith_disciplined_2018, - title = {A disciplined approach to neural network hyper-parameters: {Part} 1 -- learning rate, batch size, momentum, and weight decay}, - shorttitle = {A disciplined approach to neural network hyper-parameters}, - url = {http://arxiv.org/abs/1803.09820}, - doi = {10.48550/arXiv.1803.09820}, - abstract = {Although deep learning has produced dazzling successes for applications of image, speech, and video processing in the past few years, most trainings are with suboptimal hyper-parameters, requiring unnecessarily long training times. Setting the hyper-parameters remains a black art that requires years of experience to acquire. This report proposes several efficient ways to set the hyper-parameters that significantly reduce training time and improves performance. Specifically, this report shows how to examine the training validation/test loss function for subtle clues of underfitting and overfitting and suggests guidelines for moving toward the optimal balance point. Then it discusses how to increase/decrease the learning rate/momentum to speed up training. Our experiments show that it is crucial to balance every manner of regularization for each dataset and architecture. Weight decay is used as a sample regularizer to show how its optimal value is tightly coupled with the learning rates and momentums. Files to help replicate the results reported here are available.}, - urldate = {2025-08-05}, - publisher = {arXiv}, - author = {Smith, Leslie N.}, - month = apr, - year = {2018}, - note = {arXiv:1803.09820 [cs]}, - keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning}, - annote = {Comment: Files to help replicate the results reported here are available on Github}, - file = {Full Text PDF:/home/alex/Zotero/storage/SWJYFVTG/Smith - 2018 - A disciplined approach to neural network hyper-parameters Part 1 -- learning rate, batch size, mome.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/KWQGJ7MX/1803.html:text/html}, +@article{noauthor_birth_nodate, + title = {Birth Control Guide (Chart)}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/ATASR8BS/Birth Control Guide (Chart).pdf:application/pdf}, } -@misc{loshchilov_decoupled_2019, - title = {Decoupled {Weight} {Decay} {Regularization}}, - url = {http://arxiv.org/abs/1711.05101}, - doi = {10.48550/arXiv.1711.05101}, - abstract = {L\$\_2\$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is {\textbackslash}emph\{not\} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L\$\_2\$ regularization (often calling it "weight decay" in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by {\textbackslash}emph\{decoupling\} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at https://github.com/loshchil/AdamW-and-SGDW}, - urldate = {2025-08-05}, - publisher = {arXiv}, - author = {Loshchilov, Ilya and Hutter, Frank}, - month = jan, - year = {2019}, - note = {arXiv:1711.05101 [cs]}, - keywords = {Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Mathematics - Optimization and Control}, - annote = {Comment: Published as a conference paper at ICLR 2019}, - file = {Full Text PDF:/home/alex/Zotero/storage/KQSRMXBI/Loshchilov and Hutter - 2019 - Decoupled Weight Decay Regularization.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/AADJFDL3/1711.html:text/html}, +@article{shanmugam_multi-site_2023, + title = {A multi-site study of the relationship between photoperiod and ovulation rate using Natural Cycles data}, + volume = {13}, + issn = {2045-2322}, + url = {https://www.nature.com/articles/s41598-023-34940-z}, + doi = {10.1038/s41598-023-34940-z}, + abstract = {Abstract + Many species exhibit seasonal patterns of breeding. Although humans can shield themselves from many season-related stressors, they appear to exhibit seasonal patterns of investment in reproductive function nonetheless, with levels of sex steroid hormones being highest during the spring and summer months. The current research builds on this work, examining the relationship between day length and ovarian function in two large samples of women using data from the Natural Cycles birth control application in each Sweden and the United States. We hypothesized that longer days would predict higher ovulation rates and sexual motivation. Results revealed that increasing day length duration predicts increased ovulation rate and sexual behavior, even while controlling for other relevant factors. Results suggest that day length may contribute to observed variance in women’s ovarian function and sexual desire.}, + pages = {8379}, + number = {1}, + journaltitle = {Sci Rep}, + author = {Shanmugam, Divya and Espinosa, Matthew and Gassen, Jeffrey and Van Lamsweerde, Agathe and Pearson, Jack T. and Benhar, Eleonora and Hill, Sarah}, + urldate = {2025-09-04}, + date = {2023-05-24}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/CQC6H8DH/Shanmugam et al. - 2023 - A multi-site study of the relationship between photoperiod and ovulation rate using Natural Cycles d.pdf:application/pdf}, +} + +@article{papaioannou_quality_2013, + title = {Quality index assessment of vaginal temperature based fertility prediction and comparison with luteinising hormone testing, ultrasound folliculometry and other home cycle monitors}, + volume = {100}, + issn = {00150282}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0015028213017263}, + doi = {10.1016/j.fertnstert.2013.07.947}, + abstract = {{OBJECTIVE}: Recently, reports have emerged on an association between elevated peak e2 at the time of hcg trigger and small for gestational age ({SGA}). We seek to determine the value of peak e2 on predicting {SGA}. {DESIGN}: Retrospective Cohort Study. {MATERIALS} {AND} {METHODS}: This retrospective cohort study was drawn from a combination of our Continuum Reproductive Center’s {IVF} database and the St. Luke’s Roosevelt delivery database from 2007 to 2012. Singleton live births {\textgreater}24 weeks resulting from non-donor fresh {IVF} cycles were analyzed. A Receiver Operator Curve ({ROC}) analysis was performed on peak E2 levels on fresh cycles, Odds Ratio ({OR}) were also calculated. +{RESULTS}: A total of 204 singleton pregnancies {\textgreater}24 weeks resulted from 2105 fresh {IVF} cycles in this time period. {ROC} analysis revealed an Area Under the Curve ({AUC})¼ 0.54. The optimal cutoff point was 2209 pg/ml. {OR}¼1.82 [0.83-4.01]; p¼0.1379, for pregnancies above the cutoff point. Sensitivity: 73\% [56\%-86\%]. Specificity: 40\% [33\%-48\%]. {PPV}: 22\% [15\%-30\%]. {NPV}: 87\% [77\%-94\%]. +{CONCLUSION}: The value of peak e2 at the time of hcg trigger for prediction of {SGA} is limited. Although recent studies have shown increased Odds Ratio, in our diverse population this findings were not reproduced. Larger prospective studies are needed to correlate our findings.}, + pages = {S326--S327}, + number = {3}, + journaltitle = {Fertility and Sterility}, + author = {Papaioannou, S. and Al Wattar, B.H. and Milnes, R.C. and Knowles, T.G.}, + urldate = {2025-09-04}, + date = {2013-09}, + langid = {english}, + file = {PDF:/home/alex/Zotero/storage/J7HXJ5ZZ/Papaioannou et al. - 2013 - Quality index assessment of vaginal temperature based fertility prediction and comparison with lutei.pdf:application/pdf}, +} + +@online{noauthor_natural_nodate-1, + title = {Natural Cycles: Natural Birth Control {\textbar} No Hormones or Side Effects}, + url = {https://www.naturalcycles.com}, + shorttitle = {Natural Cycles}, + abstract = {Natural Cycles birth control is 93\% effective with typical use and 98\% effective with perfect use. Learn more about hormone-free birth control today.}, + titleaddon = {Natural Cycles}, + urldate = {2025-09-04}, + langid = {american}, + file = {Snapshot:/home/alex/Zotero/storage/HQEIAZSB/www.naturalcycles.com.html:text/html}, } diff --git a/thesis/main.tex b/thesis/main.tex index 1c05dbf..7fd374f 100644 --- a/thesis/main.tex +++ b/thesis/main.tex @@ -50,7 +50,8 @@ Alexander Blank\\[0.5cm] \textbf{Supervisor:}\\ - Prof. Bogdan Franczyk\\[1.5cm] + Prof. Bogdan Franczyk\\ + Dr. Christian Alvermann\\[1.5cm] \textbf{Date:} September 2025\\[2cm] diff --git a/thesis/resources/figures/background/background_long_cycle.png b/thesis/resources/figures/background/background_long_cycle.png index 18a4a73..1100b70 100644 Binary files a/thesis/resources/figures/background/background_long_cycle.png and b/thesis/resources/figures/background/background_long_cycle.png differ diff --git a/thesis/resources/figures/discussion/irregular_cycle_pattern_example.png 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b/thesis/resources/figures/results/temperature_drop_pattern.png new file mode 100644 index 0000000..a431aed Binary files /dev/null and b/thesis/resources/figures/results/temperature_drop_pattern.png differ diff --git a/thesis/sections/appendix.tex b/thesis/sections/appendix.tex index 106b5d7..bc60763 100644 --- a/thesis/sections/appendix.tex +++ b/thesis/sections/appendix.tex @@ -487,4 +487,5 @@ \textbf{Bold} values represent the best values across all models for a given metric.} \label{tab:regular_vs_irregular_ov_over_results} \end{table} -\end{landscape} \ No newline at end of file +\end{landscape} + diff --git a/thesis/sections/background.tex b/thesis/sections/background.tex index bc5c300..3214691 100644 --- a/thesis/sections/background.tex +++ b/thesis/sections/background.tex @@ -11,11 +11,12 @@ The menstrual cycle consists of physiological changes preparing the female body typically spanning around 28 days but varying considerably among individuals. It includes two main phases: the follicular phase, beginning with menstruation, and the luteal phase, following ovulation. -During the follicular phase, ovarian follicles mature under the influence of rising estradiol levels, thickening the uterine lining (endometrium). +During the follicular phase (Figure~\ref{fig:background_menstrual_cycle_physiology} until day 14), +ovarian follicles mature under the influence of rising estradiol levels, thickening the uterine lining (endometrium). Around mid-cycle, a surge of luteinizing hormone (LH) and follicle-stimulating hormone (FSH), triggered by peak estradiol, induces ovulation—the release of a mature egg into the fallopian tube. -After ovulation, the luteal phase begins. +After ovulation, the luteal phase begins (Figure~\ref{fig:background_menstrual_cycle_physiology} day 14 to 28). Progesterone increases substantially, maintaining endometrial thickness for potential embryo implantation. In parallel, a subtle rise in body temperature (~0.5°C) occurs due to progesterone elevation. If fertilization does not happen, progesterone and temperature decline back to baseline levels, resulting in menstruation and initiating a new cycle. @@ -55,13 +56,17 @@ The luteal phase begins at the ovulation and continues until the next menstruati While many cycles exhibit a characteristic biphasic pattern, deviations from this norm are common. -Some remain monophasic, which might be an indication for an anovulatory cycle, which is a menstrual cycle, where no ovulation occurs. +Some do not show the typical temperature surge, which might be an indication for an anovulatory cycle. +Anovulatory cycles don't have an ovulation, and thus cannot result in a pregnancy. Anovulation can have various causes, including hormonal imbalances, stress, or underlying health conditions~\cite{rosenfield_adolescent_2013}. -Monophasic cycles with a confirmed ovulation event have been observed, so there seems to be no clear indication that it is a direct cause of anovulation~\cite{moghissi_accuracy_1976}. +Monophasic cycles with a confirmed ovulation event have also been observed, so there seems to be no clear indication that it is a direct cause of anovulation~\cite{moghissi_accuracy_1976}. Thus, distinguishing between ovulatory and anovulatory cycles is challenging, as the only definitive confirmation of successful ovulation in a clinical sense is a pregnancy. Even ultrasound imaging can only confirm that an egg was released from its follicle—not whether it was successfully implanted or fertilized. -Figure~\ref{fig:background_anovulation} shows a cycle with a monophasic temperature pattern. + +Figure~\ref{fig:background_labeled_cycle} shows a cycle with a typical biphasic temperature pattern. +In contrast, Figure~\ref{fig:background_anovulation} shows a cycle with a monophasic temperature pattern. +Over time, the temperature does not show any significant or longer lasting temperature changes. To illustrate the diversity of real-world menstrual cycles, Figures~\ref{fig:background_long_cycle} and~\ref{fig:background_short_cycle} show examples of cycles that are significantly longer or shorter than a normative 28-day cycle. @@ -107,6 +112,7 @@ Fertility varies throughout the menstrual cycle, centered around the ovulation e An egg remains viable for about 24 hours post-ovulation, while sperm can survive up to 6 days in a woman's reproductive tract, thus the fertile period extends to approximately five days prior to ovulation~\cite{dunson_day-specific_1999}. Consequently, the whole fertile window generally spans six days: five days preceding ovulation and one day after. +The distribution of fertility probability is not dependent on the length of the cycle. \begin{figure}[htbp] \centering @@ -116,7 +122,8 @@ Consequently, the whole fertile window generally spans six days: five days prece \label{fig:background_pregnancy_chance} \end{figure} -Figure~\ref{fig:background_pregnancy_chance} demonstrates the probability of fertilization peaking one day before ovulation, emphasizing the critical timing for fertility prediction. +Figure~\ref{fig:background_pregnancy_chance} demonstrates the probability of fertilization peaking one day before ovulation, +emphasizing the critical timing for fertility prediction. Fertility prediction fundamentally depends on accurate ovulation timing. However, since the goal is to identify the fertile window before ovulation occurs, detection must be early and precise. @@ -141,6 +148,11 @@ pregnancy effort, causing frustration or delays, but no potential dangers to the Therefore, algorithms for this group don't need to be as conservative. It remains to be seen, where the middle ground lies and how different algorithms perform for different use cases. +In this study, we'll focus on fertility prediction, which incorporates both use cases, and thus we will not train +different models for each use-case. +However, we will test the thresholds used for decision-making to find use-case dependent optimums. +Section~\ref{subsubsec:use_case_evaluation} will introduce the methodology in more detail. + \subsubsection{Physiological Signs of Ovulation}\label{subsubsec:physiological_signs} Several physiological signs correlate with ovulation and can be used for prediction. As shown in Figure~\ref{fig:background_menstrual_cycle_physiology}, these include hormonal fluctuations (LH and FSH surges) and @@ -177,7 +189,7 @@ Thanks to a battery life of at least six months, the device supports continuous A known limitation of manual cycle annotations is the potential for misalignment. Intermediate bleeding events unrelated to menstruation (e.g., ovulatory spotting or irregular shedding) or missing menstruation entries can lead to ambiguous cycle definitions. -Therefore, all user-entered cycle starts undergo manual review to reduce annotation errors. +Manual review of all user-entered cycle starts would improve the quality of the annotations, but is currently not done. \begin{figure}[htbp] \centering @@ -198,7 +210,7 @@ contained hardware-related anomalies, or fell outside a reasonable cycle length Cycles shorter than 10 days typically result from incorrect cycle start entries or premature termination of temperature recordings. Long cycles, longer than 150 days, are often due to data entry errors or pregnancy-related recordings, -where the sensor was worn continuously throughout gestation—sometimes producing sequences up to nine months long. +where the sensor was worn continuously throughout gestation, sometimes producing sequences up to nine months long. While such cases may still contain useful information, they were excluded from this analysis to avoid complications in preprocessing and labeling. In most instances, only a small portion of these extended cycles contributes meaningfully to the study objectives. @@ -207,8 +219,10 @@ The cutoff values for cycle length are 10 and 150 days, respectively. The cleaned dataset has 40{,}266 menstrual cycles from 6{,}245 users. The median number of cycles per user is 4 (IQR: 2--8) and the median cycle length is 28 days (IQR: 26--32). -The average data density—defined as the fraction of available measurements out of the theoretical maximum of 288 measurements per day—is 0.90. +The average data density, defined as the fraction of available measurements out of the theoretical maximum of 288 measurements per day, is 0.90. This corresponds to an average data availability of 90\% per cycle, with an average loss of 10\%. +Data loss is usually caused by users not wearing the sensor for a longer time, or an erroneous sensor that needed replacement, +but was not immediately delivered. 37934 cycles (94\%) were classified as biphasic and 2333 (6\%) as monophasic. @@ -217,7 +231,7 @@ Users had a median age of 32 years (IQR: 29–36), median weight of 65 kg (IQR: \subsubsection{Irregularities and Confounding Factors} Despite careful data collection, real-world measurements are subject to physiological and behavioral noise. Especially core body temperature is influenced by various factors unrelated to the menstrual cycle. -Illnesses—especially those involving fever—can significantly affect temperature patterns. +Illnesses, especially those involving fever, can significantly affect temperature patterns. This poses a challenge for any analysis relying on temperature data, as one of the key physiological indicators of ovulation is a post-ovulatory temperature rise (see Section~\ref{subsubsec:physiological_signs}). Figure~\ref{fig:background_fever_cycle} shows an example of a cycle where an illness caused a marked increase in temperature. @@ -403,10 +417,9 @@ In the case of machine translation, this input would be a sentence in the source The input tokens are first mapped to dense continuous vector representations (embeddings). -Since the attention mechanism permutation-invariant---that is, it does not inherently encode the order of tokens in the sequence--- +Since the attention mechanism permutation-invariant, that is, it does not inherently encode the order of tokens in the sequence, \emph{positional encodings} are added to the token embeddings to provide information about the token positions in the sequence. - Without positional encoding, repeated tokens such as `The` would be indistinguishable to the model regardless of their location, even if they play different syntactic or semantic roles. Positional encodings, often based on sinusoidal functions, inject a unique position-dependent signal @@ -419,6 +432,7 @@ In this work, we use sine and cosine functions of different frequencies: \end{align} where \(i\) is the dimension of the input and \(pos\) is the position in the sequence, as in the original paper~\cite{vaswani_attention_2017}. +Positional encodings are added to the embedded inputs through simple addition. \begin{figure}[htbp] \centering @@ -562,6 +576,8 @@ These techniques reduce the computational load while maintaining salient informa Figure~\ref{fig:background_convolution_example} illustrates a simple one-dimensional convolution applied to a sequence using a filter of size 3. The stride determines how far the filter moves at each step, affecting both the resolution and length of the resulting feature map. +As a recap, resolution in this context means, how many of the daily measurements (initially 288) are retained for the input of the models. +The sequence length is the overall number of measurements available. \begin{figure}[htbp] \centering @@ -571,6 +587,6 @@ The stride determines how far the filter moves at each step, affecting both the \end{figure} In summary, time-series modeling offers a range of approaches, each with specific trade-offs. -RNNs and LSTMs provide explicit sequential modeling but suffer from training inefficiencies. +RNNs and LSTMs provide explicit sequential modeling but suffer from vanishing gradients, especially for longer sequences. Transformers excel at long-range context capture but demand more memory and parallelization. -Convolutional layers offer efficient local feature extraction and often serve as useful pre-processing stages for both model families. \ No newline at end of file +Convolutional layers might offer efficient local feature extraction and might mitigate the shortcomings of both architecture types. \ No newline at end of file diff --git a/thesis/sections/discussion.tex b/thesis/sections/discussion.tex index 30262de..a557371 100644 --- a/thesis/sections/discussion.tex +++ b/thesis/sections/discussion.tex @@ -4,6 +4,165 @@ \section{Discussion}\label{sec:discussion} +\subsection{RQ1: Model Architecture Evuation an Optimization}\label{subsec:discussion_rq1_model_evaluation} + + +The first research objective considered different model architectures and how they perform +under various hyperparameter configurations, with respect to the optimization goals of this study. + +We found that non-convolutional models performed best with medium resolutions and longer input windows, +where they also outperformed their convolutional counterparts. +This suggests that including more historical context improves prediction accuracy. +However, there appears to be a trade-off: +while more information can improve predictions, it also introduces additional noise, which the models struggle to handle effectively. +The strong performance at medium resolutions indicates a possible sweet spot—balancing information richness with model capacity and generalization. + +The convolutional variants performed best with medium-length input windows, which was somewhat surprising. +One would expect convolutional downsampling to enable more efficient data representation, +allowing the model to extract relevant patterns from high-resolution data and to perform better on longer sequences. +However, this was not observed in our experiments. +Instead, convolutional models performed worse than non-convolutional models on longer input windows, +suggesting that the benefits of downsampling may be offset by limitations in capturing long-range dependencies. +For medium and short input windows, however, convolutional models did outperform the non-convolutional +variants—indicating that the convolution itself provides an advantage when input length is limited. + +To our knowledge, no prior studies have compared different deep learning architectures for real-time fertility prediction. +This highlights both the novelty and the exploratory nature of our approach. + +All these results must be interpreted with caution, as they are based on single-shot experiments and are not statistically robust. +A more rigorous evaluation would involve training all models multiple times to minimize the +impact of random initialization and other stochastic processes in training. +For most metrics, differences in performance were small, often less than 1\%, and could easily be attributed to such variability. + +We are also aware of potential losses in predictive quality due to class imbalance in the labels. +Especially in long cycles, the targets are mostly zero, except for a narrow window around ovulation +(for the fertility-probability target) or the area after ovulation (for the ov-over target). +Positive-to-negative target ratios can reach 10–20:1 in long cycles, meaning for each non-zero target day, +there are 10 to 20 days with all-zero targets. +This imbalance may introduce instability during training and reduce predictive performance. +Future iterations of this study could incorporate class-weighting or loss-balancing mechanisms to address this issue. + +Future work should include a more thorough parameter search and statistically more robust evaluation methodology. +A more sophisticated hyperparameter search may be necessary to identify globally optimal configurations for each model architecture. +Time and resource constraints could be alleviated using more efficient search algorithms such as Bayesian Optimization, +Genetic Algorithms, or Neural Architecture Search (NAS) to better explore the joint parameter space. + +Additionally, we only focused on basic models—LSTMs and Transformers, with convolutional hybrids. +A closer look into alternative architectures, or tailoring architectures more specifically +to the characteristics of menstrual cycle data, could significantly improve predictions. +Recent Transformer variants for time series modeling, such as \emph{TimeXer}~\cite{wang_timexer_2024} +or \emph{MAMBA}~\cite{wang_is_2024}, may offer better performance. +Alternatively, a custom architecture could be developed to reflect the domain-specific structure of biological temperature data more closely. +This could be combined with a more sophisticated convolutional setup or preprocessing strategy +to handle high-resolution inputs more efficiently. +Such preprocessing might help overcome computational bottlenecks that raw LSTMs and Transformers encounter when dealing with large input sequences. + +Moreover, a more statistically grounded approach to feature selection could further improve predictive performance. +Both raw and engineered features could be evaluated for their impact on model output. +Especially promising are intermediate features such as cycle-level aggregates, +temperature accumulation over time or space, and static user-level characteristics. +The database used in this study also contains an extensive set of user-logged marker events, such as intercourse, +intermediate bleeding, or illnesses, which could help the models learn correlations between these events and temperature fluctuations. + +Finally, exploring alternative prediction targets may help address some of the core challenges of fertility prediction. +Beyond the fertility-probability and ov-over indicators used in this study, +targets such as the number of days to the next ovulation (or since the last), or the ovulation day +as a direct regression target, might prove more stable and informative. +As we will see in the next section, the current targets are highly sensitive to anomalies, +which may not be the case for alternative formulations. + +\subsection{RQ2: Factors and Patterns that Influence Prediction}\label{susbsec:discussion_rq2_factors_and_patterns} + +Our second research objective focused on identifying potential factors and patterns in the data +that influence fertility prediction. + +To our knowledge, no prior work has explicitly analyzed the relationship between temperature patterns +and ovulation—including the corresponding fertile window—in a way that could directly inform machine learning models. +This underscores the exploratory nature of this analysis. + +We identified a consistent pre-ovulatory temperature dip that aligned closely with the ground-truth fertility curve. +This drop was particularly pronounced in short and regular cycles. +In contrast, irregular cycles often exhibited too much variability and noise in the follicular phase +for the models to reliably detect this temperature drop, or to associate it meaningfully with increased fertility probability. +Interestingly, the magnitude of the temperature dip was strongly correlated with the predicted fertility probability: +larger dips tended to produce higher model confidence. + +This observed correlation may be coincidental, or it may indicate a meaningful biological marker +for fertility and successful ovulation. +We hypothesize that there is a real association between the magnitude of the pre-ovulatory temperature dip and the likelihood of ovulation. + +However, our method has a critical limitation: the ground-truth labels used were not based on clinically confirmed ovulation events. +This limitation may result not only in fertility-probability curves that are arbitrarily offset from their true value, +but also in the inclusion of cycles labeled as ovulatory that in fact were anovulatory. +As shown in Section~\ref{subsubsec:physiological_signs}, the only reliable indicator +of ovulation is daily transvaginal ultrasound, which was not feasible given the scale of our dataset. + +Future studies could apply the current model to a smaller subset of cycles with clinically +confirmed ovulation and/or cycles exhibiting a clear temperature rise, to test the hypothesis +that a significant pre-ovulatory temperature drop is either necessary for ovulation or strongly correlated with fertility. + +This approach may also help clarify the role of temperature anomalies in fertility prediction, +for example, the sharp spike shown in Figure~\ref{fig:results_rq2_anomaly_in_temperature_drop}. +Anomalies like this, which may be due to illness with fever symptoms, likely affect both +the menstrual cycle and the model's ability to detect pre-ovulatory fertility. +This case highlights that body temperature is highly sensitive to external physiological factors +that cannot be inferred from the temperature curve alone. + +It is likely that many additional patterns or confounding factors in +the temperature data directly or indirectly affect model predictions. +Future research could explore this further, ideally in collaboration with experts in reproductive health. +This could lead to a more biologically grounded interpretation of the patterns uncovered by the models. + +Moreover, improvements in label quality will naturally improve the interpretability and accuracy of predictions. +Better labels would also allow more reliable medical interpretation of the prediction outputs and the patterns driving them, as previously discussed. + +\subsection{RQ3: Performance Across Regular and Irregular Cycles}\label{subsec:discussion_rq3_regular_vs_irregular} + +The third research objective of this study was to compare model performance across subsets of users +with regular and irregular menstrual cycles. + +We found that predictive performance is clearly sensitive to cycle irregularity. +Both overall accuracy and the improvement in performance over a growing user history +were substantially better for regular cycles compared to irregular ones. +This suggests that past cycles contain valuable information that helps the models +predict the current cycle more reliably, especially when those past cycles follow consistent patterns. + +Some of this improvement can also be attributed to static features, several of which are +derived from aggregated past cycles, such as average cycle length or average ovulation day. +These features appear to be more informative and stable in the regular cycle group. + +Based on the clear difference in performance, we hypothesize, that all models rely more on those static features +than on patterns identified in the temperature curves, since if that were the case, the difference would be smaller +or non-existent. +There might be additional factors that differentiate regular from irregular cycles, that might explain the results, +which have not been identified yet. +This could also point out, that it is hard or even impossible to reliably predict fertility based on temperature alone, +but this hypothesis needs to be investigated in further research. + +\citeauthor{yu_tracking_2022} also report a significant drop in performance for irregular cycles +in their study~\cite{yu_tracking_2022}, particularly in terms of sensitivity. +While we cannot directly compare results due to methodological differences, especially since +our models do not perform explicit binary classification into fertile vs.\ non-fertile days, a similar trend is observable. +In our case, the models tend to overestimate fertility probability when faced with uncertainty, particularly in irregular cycles +(see Figure~\ref{fig:results_rq2_irregular_cycle_predictions_example}). +As a result, we do not report specificity or sensitivity values. +However, the use-case scenarios in the next section offer a more binary evaluation framework. + +A promising direction for future work would be to further investigate the causes of +performance degradation in irregular cycle groups. +One potential experiment could involve removing static features derived from past cycles to evaluate whether the models +are genuinely using the temperature patterns from previous cycles that fall within +the current input window, or relying mainly on those engineered features. + +It may also be valuable to explore the effect of increasing the input window length further. +Ideally, the model would have access to the entire cycle history of a user for training and decision-making. +However, this would likely exceed the memory and capacity constraints of the architectures used in this study. +Overcoming this limitation may require more efficient or hierarchical time series architectures, as previously discussed. + +\subsection{RQ4: Use-case Evaluations}\label{subsec:discussion_rq4_use_case_evaluations} + + +% ------------------------------------------------------------------------------------- This study set out to evaluate the predictive value of body temperature for ovulation and fertility, to assess the performance of different model architectures across use cases, and to compare machine learning models with rule-based baselines. @@ -12,21 +171,24 @@ Below we discuss the findings in relation to these objectives, their implication \subsection{Predictive Value of Body Temperature} Our results confirm that body core temperature carries meaningful predictive value for ovulation and fertility, particularly in regular cycles with clear pre-ovulatory dips. +This dip before the In such cases, fertility-probability curves aligned closely with the ground truth, demonstrating that models can reliably exploit this physiological marker. -However, anomalies in the signal—such as irregular spikes or absent dips—frequently led to erroneous predictions. +However, anomalies in the signal, such as irregular spikes or absent dips, frequently led to erroneous predictions. This suggests that the models strongly rely on short-term temperature fluctuations without distinguishing between ovulation-related and unrelated changes. The irregular-versus-regular cycle analysis reinforces this interpretation: prediction accuracy was consistently higher in regular cycles, indicating that models leverage cyclical regularity in addition to absolute thermal changes. +Especially for regular cycles, the performance of the trained models + +%[continue here: bring in your figure-based examples of temperature drops, anomalies, and unclear cases, with interpretation rather than description.] + Taken together, these findings highlight both the utility and the limitations of body temperature as a single biomarker. While it provides a strong signal in favorable cases, its variability across users and cycles limits robustness in real-world applications. Additional biomarkers or contextual information are likely required to disambiguate genuine ovulatory patterns from noise. -%[continue here: bring in your figure-based examples of temperature drops, anomalies, and unclear cases, with interpretation rather than description.] - \subsection{Model Architectures and Use-Case Performance} Across architectures, LSTMs consistently outperformed both Transformers and convolutional models in the fertility-probability task. This was somewhat unexpected given the recent dominance of Transformer-based approaches in sequence modeling. @@ -49,44 +211,6 @@ For pregnancy planning, results were less directly comparable, reflecting the mu Nonetheless, the analysis illustrates how probabilistic outputs could be adapted to individual risk preferences, highlighting the flexibility of machine learning approaches over fixed rules. -%[continue here: expand with more detail on threshold effects, how different models fared in contraception vs. pregnancy contexts, and the implications for user-facing tools.] - -\subsection{Comparison to Rule-Based Baselines} -Compared with simple heuristic baselines, all machine learning models demonstrated superior predictive performance, -particularly in irregular cycles where rule-based approaches break down. -This highlights the advantage of data-driven methods, which can learn subtle patterns and adapt to user-specific variability -that static rules cannot capture. -Nevertheless, rule-based methods retain value for their simplicity and interpretability, -and could complement machine learning models in hybrid approaches where transparency is essential. - -%[continue here: connect this explicitly to natural family planning methods in the literature, and comment on where ML truly adds value.] - -\subsection{Limitations} -Several limitations constrain interpretation. -First, results were reported on aggregate test sets without uncertainty quantification; -per-user predictions were not retained, preventing bootstrap confidence intervals or paired statistical testing. -Second, labels were generated by a retrospective algorithm trained on expert annotations. -Any inaccuracies in this algorithm propagate directly into the training data and may bias model learning. -Third, reliance on temperature alone leaves the models vulnerable to anomalies caused by illness, lifestyle, or measurement error. -Finally, resource constraints limited the scope of hyperparameter optimization and the exploration of more advanced architectures. - -%[continue here: add dataset-specific limitations such as age range, long-cycle imbalance, and lack of prospective evaluation.] - -\subsection{Future Work} -Future extensions of this work should pursue several directions. -Incorporating additional user-entered markers—such as bleeding, stress, illness, -or intercourse—may provide critical context to disambiguate temperature anomalies. -Alternative target formulations, such as predicting the day of ovulation or the time-to-ovulation, -could reduce class imbalance and better reflect clinical needs. -Training separate models for fertility and ovulation-over tasks may also improve performance by reducing task interference. - -From a methodological perspective, exploring architectures tailored for time-series forecasting—such as -TimeXer or MAMBA—alongside principled hyperparameter optimization techniques (e.g., Bayesian optimization, NAS) could yield further gains. -Most importantly, future studies should evaluate these approaches prospectively, -with clinically validated ovulation labels and diverse populations, -to establish their real-world utility in natural family planning and contraception. - -%[continue here: add your own vision for clinical applications, e.g. integration into fertility apps, medical oversight, or regulatory implications.] %\section{Discussion}\label{sec:discussion} @@ -126,16 +250,6 @@ for potential users of the predictions. All prediction curve in this section have been generated with the best model selected in section~\ref{subsubsec:results_best_model_config_selection}, i.e., the LSTM model. - -\begin{figure}[htbp] - \centering - \includegraphics[width=1.0\textwidth]{resources/figures/discussion/regular_cycle_fertility_prediction} - \caption{ - Temperature rolling average and fertility-probability prediction for a user with a regular cycle pattern. (Values are scaled features) - } - \label{fig:discussion_regular_cycle_fertility_prediction} -\end{figure} - Figure~\ref{fig:discussion_regular_cycle_fertility_prediction} shows the prediction curve for the fertility-probability target for a user with a regular cycle pattern. For such a regular cycle pattern, the predictions almost exactly match the targets. @@ -166,33 +280,6 @@ The identified temperature drop and the fertility that seems to come with it cou with more accurate ovulation labeling. It might be an indicator for a successful upcoming ovulation. -\begin{figure}[htbp] - \centering - \includegraphics[width=1.0\textwidth]{resources/figures/discussion/temperature_drop_fertility} - \caption{ - Temperature rolling average and fertility-probability prediction with a clear correlation between the temperature drop - pre-ovulation and the fertility. (Values are scaled features) - } - \label{fig:discussion_temperature_drop_fertility_prediction} -\end{figure} -\begin{figure}[htbp] - \centering - \includegraphics[width=1.0\textwidth]{resources/figures/discussion/temperature_spike_in_temperature_dop} - \caption{ - Temperature rolling average and fertility-probability prediction with a disruptive temperature spike during the pre-ovulatory phase, - leading to an early end of the predicted fertile window. - } - \label{fig:discussion_spike_in_temperature_drop} -\end{figure} -\begin{figure}[htbp] - \centering - \includegraphics[width=1.0\textwidth]{resources/figures/discussion/temperature_unclear_temperature_drop} - \caption{ - Temperature rolling average and fertility-probability prediction with no clear temperature drop and a resulting - incorrect prediction. (Values are scaled features) - } - \label{fig:discussion_unclear_temperature_drop} -\end{figure} The results of the irregular vs regular cycle study, as shown in~\ref{subsubsec:regular_vs_irregular_cycles}, indicate, that the models seem to learn recurrent cycle pattern on a per-user basis. @@ -240,53 +327,6 @@ While the results themselves can give a clear indication of both the fertility p of a given cycle is already over for any given day, there a variety of external factors that should be taken into consideration for a direct output to the user. -\subsection{Limitations}\label{subsec:limiations} -This analysis reports aggregate test-set metrics without uncertainty quantification. -Because we did not retain per-user predictions, we cannot compute user-level bootstrap confidence intervals or perform paired significance testing. -As a result, apparent performance differences, especially small ones, may reflect sampling variability. -Future re-evaluation that stores per-user predictions will enable user-level bootstrapping, calibration assessment, and formal comparisons. - -\subsection{Future Work}\label{sec:future_work} -There are several directions in which this study could be extended, -most of which were omitted due to time and resource constraints but represent valuable areas for future exploration. - -One major area is feature selection. -The dataset used includes additional user-entered markers such as physiological signs (e.g., bleeding, illness, stress) -and external events (e.g., intercourse, pregnancy tests). -These markers were not included in the present analysis but may carry predictive value and could meaningfully improve model performance. -Similarly, the introduction of engineered or intermediate features, derived from raw inputs, may help models better -capture relevant patterns and temporal dependencies. -Additionally, the target features could be modelled in a better way, as, especially for long cycles, -there is a large imbalance of value distribution. -If a cycle has a length of 100 days with an ovulation at day 90, only 10\% of the ovulation-over targets are one. -The same applies to the fertility target, which will be zero throughout almost the whole sequence, -which will make it harder for the models to learn useful information. - -Alternative target formulations could also be explored to better reflect the structure of the fertile window and ovulation. -For example, instead of predicting a daily fertility probability, models could aim to identify the absolute day of ovulation, -or estimate the time until the next (or since the last) ovulation event. -Additionally, multiple models could be trained individually for each target, to avoid confusing the models -on two different prediction tasks. -While the fertility-probability and the timing of the ovulation have an inherent causal relationship, -there might be anomalies in the data that confuse one or both targets, and independent predictions might perform better. - - -As noted in Section~\ref{subsubsec:data_labeling}, ovulation labels were assigned using a -retrospective algorithm trained on expert-annotated data. -Any inaccuracies in this algorithm propagate directly to the supervised learning labels. -Thus, improving prediction quality may require a newly labeled dataset—ideally combining expert review with algorithmic assistance. - -Finally, while the models used in this study (LSTMs and Transformers) are well-established for time-series analysis, -they were not extensively customized. -Future work could involve tailoring architectures more specifically to the characteristics of menstrual cycle data. -Recent transformer variants designed for time series, such as \emph{TimeXer}~\cite{wang_timexer_2024} or \emph{MAMBA}~\cite{wang_is_2024}, -may offer improved performance. -Alternatively, a custom architecture could be developed to better reflect the domain-specific structure of biological temperature data. - -Future work may incorporate more advanced hyperparameter optimization techniques, -such as Bayesian Optimization, Genetic Algorithms, or Neural Architecture Search (NAS), -to better explore the joint parameter space in a more efficient and principled manner. - % talk about whether bbt / temperature can be used for such a task, discuss bbt doubt papers % While previous work has argued against the predictive value of BBT~\cite{some_author_2010}, our findings suggest otherwise. diff --git a/thesis/sections/introduction.tex b/thesis/sections/introduction.tex index 05aa273..1c83b1b 100644 --- a/thesis/sections/introduction.tex +++ b/thesis/sections/introduction.tex @@ -8,17 +8,15 @@ While textbooks often describe a menstrual cycle as lasting 28 to 30 days with o such regularity is the exception rather than the rule~\cite{munster_length_1992, bull_real-world_2019}. For individuals with consistent cycle patterns, simple calendar-based predictions may suffice. However, for the majority, especially with increasing age and associated irregularity, more sophisticated methods are necessary. -This, combined with an ever higher age of pregnancy in industrialized and industrializing countries, -underscores the growing need for accurate understanding of the menstrual cycle~\cite{sauer_reproduction_2015}. +This, combined with the rising maternal age in industrialized and industrializing countries, +underscores the growing demand for accurate, individualized menstrual cycle prediction methods~\cite{sauer_reproduction_2015}. -For many women, the practical use cases of menstrual cycle monitoring are \emph{Natural Family Planning} (NFP) and contraception~\cite{earle_use_2021}. +For many individuals, the practical use cases of menstrual cycle monitoring are \emph{Natural Family Planning} (NFP) and contraception~\cite{earle_use_2021}. For these use cases, it is essential to identify the ovulation and its corresponding fertile and infertile days in a cycle, to either avoid or achieve pregnancy more effectively. -Ovulation is the process in which an egg cell is released from the ovaries, making fertilization possible. -This process is regulated by hormonal changes, including fluctuations in luteinizing hormone (LH) and -follicle-stimulating hormone (FSH), and is accompanied by other physiological changes such as an increase in electrical resistance -and viscosity of the cervical mucus or an increase in body temperature~\cite{wallach_prediction_1980}. +Ovulation, the release of an egg cell from the ovaries, is triggered by hormonal changes and accompanied +by physiological shifts such as changes in cervical mucus and a rise in body temperature~\cite{wallach_prediction_1980}. These processes remain incompletely understood and are influenced by lifestyle factors such as stress, diet, exercise, or health-related conditions such as Polycystic Ovary Syndrome (PCOS), making ovulation difficult to predict. @@ -49,10 +47,17 @@ NFP and contraception. Finally, we demonstrate that high predictive accuracy on highly regular, curated datasets, as commonly reported in prior work, may overestimate real-world applicability, since such datasets tend to favor even simple, rule-based approaches. +The main research objective is to evaluate the fundamental feasibility of predicting fertility and +ovulation from body temperature data using machine learning, +with a focus on how prediction performance varies across menstrual cycle types and real-world use cases. -The research objectives are: +These objectives support the broader question of whether temperature-based models can provide robust predictions +across real-world variability in cycle patterns and user needs: \begin{itemize} - \item To evaluate the predictive value of body temperature for ovulation and fertility across diverse menstrual cycle types. - \item To assess the performance of different model architectures on the use cases of natural family planning and contraception, across both regular and irregular cycles. - \item To compare sophisticated machine learning models with simple rule-based baseline approaches. + \item To train and evaluate a set of machine learning models for ovulation and fertility prediction, + using standard performance metrics such as MAE and MSE\@. + \item To identify influential factors and patterns that affect the prediction. + \item To compare prediction performance across regular and irregular cycles to assess how cycle variability affects feasibility. + \item To evaluate model outputs in the context of practical use cases, + such as contraception and natural family planning, using task-specific evaluation criteria. \end{itemize} diff --git a/thesis/sections/methodology.tex b/thesis/sections/methodology.tex index 33a0297..05ed43a 100644 --- a/thesis/sections/methodology.tex +++ b/thesis/sections/methodology.tex @@ -10,6 +10,8 @@ To address these limitations, we develop a data-driven framework based on a larg heterogeneous dataset of real-world menstrual cycles. Our approach emphasizes model transparency, adaptability to irregular patterns, and the predictive utility of high-resolution core body temperature measurements. +The overall goal is to train a model to predict the fertility-probability and information about the ovulation for +a given day, only relying on past information prior to that day. This section outlines the methodology used, including preprocessing, labeling, feature extraction, and model architectures. \subsection{Data Preprocessing}\label{subsec:data_preprocessing} @@ -64,15 +66,15 @@ The next section details how these labels are incorporated into feature represen The features used as model inputs have been divided into three categories: \begin{itemize} \item \textbf{Static features} - Characteristics, that remain constant across a user's cycle, such as age, height, or average ovulation day - \item \textbf{Known features} — Inputs known a priori at each time step, such as time of day or calendar-based variables. - \item \textbf{Observable features} — Inputs available at the current time step, including raw and derived temperature values. + \item \textbf{Known features} — Inputs known a priori at each time step, such as time of day or calendar-based variables (e.g., month of the year). + \item \textbf{Observable features} — Inputs available at the current time step, including raw and derived temperature values (e.g, rolling averages). \end{itemize} The target variables predicted by the model—like ovulation status or fertility probability—are described separately. Each feature type can handle categorical and continuous features. This allows for mixed inputs, such as scalar measurements and class labels, within the same category. -The categorization into four feature types is intended to clarify the conceptual roles of different input types. +The categorization into three feature types is intended to clarify the conceptual roles of different input types. While the current models concatenate all features into a single input stream, the distinction allows for flexibility—future models may process each feature group differently depending on their architectural design. @@ -106,11 +108,12 @@ Static features provide user-specific context that helps the model learn individ %\end{table} %Table~\ref{tab:static_features} shows all static features and their descriptions. -Prior research by \citeauthor{li_menstrual_2023} has shown that menstrual cycle characteristics vary significantly with age and BMI~\cite{li_menstrual_2023}. +Menstrual cycle characteristics vary significantly with age and BMI~\cite{li_menstrual_2023}. Including such information is therefore expected to improve predictive performance. -In addition, summary statistics from previous cycles—such as ovulation timing, temperature levels, or the fraction of ovulatory cycles—provide useful individual context. +In addition, summary statistics from previous cycles, such as ovulation timing, temperature levels, or the fraction of ovulatory cycles, provide useful individual context. These features help the model learn subject-specific variability and better estimate the likelihood and timing of ovulation in the current cycle. +If no user-specific context is available yet, population-based mean values are used. Table~\ref{tab:feature_overview} shows the full list of static input features. All historical features are computed using only data available prior to the current cycle, ensuring no data leakage and supporting robust, user-adaptive learning. @@ -141,7 +144,7 @@ The cyclical nature of the variable is clearly visible in the transformation. Although the model architectures used are sequential, the explicit inclusion of these time features allows the models to interpret each time step in a broader context. More importantly, they enable the detection of gaps in the recording, which would otherwise not be visible from the data alone. -Additionally, prior research has shown that the menstrual cycle may be influenced by weekly rhythms~\cite{ecochard_menstrual_2024}. +Menstrual cycles can be influenced by weekly rhythms~\cite{ecochard_menstrual_2024}. For example, menstruation has been found to begin more frequently on Thursdays or Fridays, suggesting that behavioral or social factors may modulate certain events in the cycle. Including this information could therefore improve the predictive quality of the models. @@ -242,20 +245,44 @@ The model outputs represent data-driven estimates and do not constitute medical \label{tab:feature_overview} \end{table} +\subsubsection{Time-Series Input Representation} +\label{subsubsec:time_series_input_representation} All features were normalized based on their empirical distributions. A \textit{standard scaler} was applied to approximately normal features without outliers, a \textit{robust scaler} was used for distributions with outliers, and a \textit{MinMax scaler} was used for all others. Different Scalers were used for the train, validation and test sets to avoid data leakage. -For the final data matrix, all features were stacked per timestep. -The static features were repeated for each timestep. +For the final data matrix, all features are stacked per timestep. +The static features are repeated for each timestep. We are aware of possible inefficiencies here. A side channel for static features might improve predictive efficiency and potential quality, but this was left out to keep the interfaces the same for compatibility purposes between all tested models -\subsubsection{Time-Series Input Representation} -\label{subsubsec:time_series_input_representation} +Let +\begin{itemize} + \item \( T \) be the sequence length (number of time steps) + \item \( x_t^{\text{obs}} \in \mathbb{R}^{d_{\text{obs}}} \): observable features at time \( t \) + \item \( x_t^{\text{known}} \in \mathbb{R}^{d_{\text{known}}} \): known features at time \( t \) + \item \( x^{\text{static}} \in \mathbb{R}^{d_{\text{static}}} \): static features (repeated across time steps) +\end{itemize} + +Then each input token at time \( t \in \{1, \dots, T\} \) is: +\[ + x_t = \left[ x_t^{\text{obs}} \;\middle|\; x_t^{\text{known}} \;\middle|\; x^{\text{static}} \right] + \in \mathbb{R}^{d_{\text{obs}} + d_{\text{known}} + d_{\text{static}}} +\] + +The full input sequence is then represented as a matrix: +\[ + X = \begin{bmatrix} + x_1 \\ + x_2 \\ + \vdots \\ + x_T + \end{bmatrix} + \in \mathbb{R}^{T \times (d_{\text{obs}} + d_{\text{known}} + d_{\text{static}})} +\] The high temporal resolution of the temperature data, 288 measurements per day, results in very long input sequences that are impractical for most deep learning models to process directly. @@ -269,9 +296,6 @@ where values from distinct categories (e.g., hours 23 and 0) might otherwise be The effect of different sampling resolutions and aggregation strategies is evaluated in Section~\ref{sec:results}. -To simulate real-time prediction rather than retrospective analysis, a sliding-window approach is employed. -This allows the model to make predictions based only on data available up to a specific point in the cycle. - To simulate real-time prediction, each cycle is split into overlapping, fixed-length input windows that capture all available data up to a given time step. As the cycle progresses, these windows slide forward, allowing the model to update its prediction based on growing historical context. @@ -318,6 +342,9 @@ Ultimately, the models used in this study were selected based on their ability t \item Generalize across users while incorporating personalized cycle context \end{itemize} +In this section we will introduce the base architectures \textbf{Long-Short-Term-Memory Models} and \textbf{Transformer Models}. +Additionally, we'll show, how the convolutional hybrids extend their functionality. + \subsubsection{LSTM-Architecture}\label{subsubsec:lstm_architecture} \begin{figure}[htbp] \centering @@ -373,7 +400,7 @@ Since the task does not require sequence-to-sequence modeling, only the encoder Its output—one vector per input token—is aggregated via 1D adaptive average pooling, resulting in a single vector representation per sequence. This vector is then passed through a linear projection layer to produce the two target outputs: fertility probability and ovulation-over indicator. -Note, that in contrast to the original use case of machine-translation, not special tokens are necessary here, as we do not perform sequence-to-sequence prediction. +Note, that in contrast to the original use case of machine-translation, no special tokens are necessary here, as we do not perform sequence-to-sequence prediction. Figure~\ref{fig:methodology_transformer_architecture} shows the overall architecture. The stacked inputs and outputs indicate batch processing. @@ -434,11 +461,20 @@ The models were trained using a configurable framework developed specifically fo allowing for flexible experimentation with different architectures, input feature sets, and hyperparameter configurations. -The training process is organized into distinct \textit{runs}, each representing a set of model experiments with a shared base configuration. -Within a run, variable parameters—such as input sequence length, hidden layer size, dropout rate, -or specific feature subsets—are systematically swept across predefined value ranges. +The training process is organized into distinct \textit{runs}, each representing a set of model experiments +(e.g., a sweep over possible input sequence lengths), with a shared base configuration (e.g., fixed input resolution for the input sequence length sweep). +Within a run, variable parameters, such as input sequence length, hidden layer size, dropout rate, +or specific feature subsets, are swept across predefined value ranges. -For each combination of parameters, a dedicated training and evaluation procedure is performed. +We define 3 runs for each base architecture and 2 for each convolutional architecture. +\begin{itemize} + \item \textbf{Input-Sequence-Length Run:} explores different input sequence lengths + \item \textbf{Input Resolution Run:} explores different input resolutions (only for base architectures) + \item \textbf{Model Parameter Run:} explores different model hyperparameters, e.g., hidden layer size +\end{itemize} + +For each combination of parameters (e.g., 20 day input sequence length, resolution of 12 values per day), +a dedicated training and evaluation procedure is performed. This structure supports efficient hyperparameter exploration and ensures consistent, reproducible training conditions across models. @@ -576,7 +612,8 @@ Each model was trained for up to 30 epochs, with early stopping based on validat To meaningfully compare model performance, we define a set of metrics according to the research objectives, that capture both overall accuracy and behavior at key points in the prediction sequence as well as cover the use cases introduced in~\ref{subsubsec:practical_use_cases}. -This includes metrics for different temporal segments, enabling a more detailed understanding of model strengths and limitations. +This includes metrics for different temporal segments, such as before and after the ovulation, +enabling a more detailed understanding of model strengths and limitations. \subsubsection{Baseline Comparisons}\label{subsubsec:baseline_comparisons} @@ -595,30 +632,32 @@ In many cases, it remains unclear whether proposed models genuinely outperform s \subsubsection{Evaluation Metrics}\label{subsubsec:evaluation_metrics} -The base metric used for all categories is the mean absolute error (MAE), -which describes the average absolute deviation of the prediction from the target value, +The base metric used for all categories is the mean squared error (MAE), +which describes the average squared deviation of the prediction from the target value, and is defined as: +\begin{align} + \text{MSE} = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 +\end{align} + +where \(y_i\) is the observed value and \(\hat{y}_i\) the predicted value. + +MSE penalizes larger errors more heavily, making it useful for highlighting substantial deviations. +This is particularly relevant for model comparison, where disproportionate errors can skew performance. +Moreover, since the fertility probability target was trained using an MSE-based loss function, +this metric directly reflects the optimization objective. + +To complement this, we also report the mean squared error (MAE): \begin{align} \text{MAE} = \frac{1}{n} \sum_{i=1}^{n} \left| y_i - \hat{y}_i \right| \end{align} - where \(y_i\) is the observed value and \(\hat{y}_i\) the predicted value. MAE was selected for its intuitive interpretability. In particular, the fertility probability target lends itself well to an absolute error interpretation, making MAE a natural choice for evaluating prediction accuracy. - -To complement this, we also report the mean squared error (MSE): -\begin{align} - \text{MSE} = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 -\end{align} -where \(y_i\) is the observed value and \(\hat{y}_i\) the predicted value. - -MSE penalizes larger errors more heavily than MAE, making it useful for highlighting substantial deviations. -This is particularly relevant for model comparison, where disproportionate errors can skew performance. -Moreover, since the fertility probability target was trained using an MSE-based loss function, -this metric directly reflects the optimization objective. +However, we will only be using the MAE as a secondary metric, as we will base our further interpretation of +the model performances on the use-case evaluations, that provide inherent real-world interpretability. We considered including the coefficient of determination (\(R^2\)) as an evaluation metric. However, we found that the target windows frequently exhibited very low variance, @@ -652,20 +691,20 @@ Tables~\ref{tab:fertility_mae_metrics} and~\ref{tab:ov_over_mae_metrics} summari \toprule \textbf{Metric Name} & \textbf{Description} \\ \midrule - \multicolumn{2}{@{}l}{\textbf{Mean Absolute Error}} \\ - \midrule - Fertility Overall & MAE over the entire sequence. \\ - During-Fertility & MAE during the fertile phase. \\ - Non-Fertility & MAE on the non-fertile days. \\ - \midrule \multicolumn{2}{@{}l}{\textbf{Mean Squared Error}} \\ \midrule Fertility Overall & MSE over the entire sequence. \\ During-Fertility & MSE during the fertile phase. \\ Non-Fertility & MSE on the non-fertile days. \\ + \midrule + \multicolumn{2}{@{}l}{\textbf{Mean Absolute Error}} \\ + \midrule + Fertility Overall & MAE over the entire sequence. \\ + During-Fertility & MAE during the fertile phase. \\ + Non-Fertility & MAE on the non-fertile days. \\ \bottomrule \end{tabular} - \caption{Evaluation metrics of the fertility probability target based on mean absolute error (MAE) and mean squared error (MSE) at various intervals across the predicted fertility window.} + \caption{Evaluation metrics of the fertility probability target based on mean squared error (MSE) and mean absolute error (MAE) at various intervals across the predicted fertility window.} \label{tab:fertility_mae_metrics} \end{table} @@ -677,20 +716,20 @@ Tables~\ref{tab:fertility_mae_metrics} and~\ref{tab:ov_over_mae_metrics} summari \toprule \textbf{Metric Name} & \textbf{Description} \\ \midrule - \multicolumn{2}{@{}l}{\textbf{Mean Absolute Error}} \\ - \midrule - OV-Over Overall & MAE over the entire sequence. \\ - Pre-OV & MAE before the ovulation. \\ - Post-OV & MAE after the ovulation. \\ - \midrule \multicolumn{2}{@{}l}{\textbf{Mean Squared Error}} \\ \midrule OV-Over Overall & MSE over the entire sequence. \\ Pre-OV & MSE before the ovulation. \\ Post-OV & MSE after the ovulation. \\ + \midrule + \multicolumn{2}{@{}l}{\textbf{Mean Absolute Error}} \\ + \midrule + OV-Over Overall & MAE over the entire sequence. \\ + Pre-OV & MAE before the ovulation. \\ + Post-OV & MAE after the ovulation. \\ \bottomrule \end{tabular} - \caption{Evaluation metrics of the ovulation-over target based on mean absolute error (MAE) and mean squared error (MSE) at various intervals across the predicted fertility window.} + \caption{Evaluation metrics of the ovulation-over target based on mean squared error (MSE) and mean absolute error (MAE) at various intervals across the predicted fertility window.} \label{tab:ov_over_mae_metrics} \end{table} @@ -701,8 +740,9 @@ This step was necessary to keep the computational effort manageable, as exhaustively testing all possible configurations for every subsequent metric would have been prohibitively expensive. Selection was based primarily on the \textbf{Fertility-Overall MSE} metric, -as it most directly reflects the main objective of this study: predicting fertility. -In cases where the difference between configurations was small, +as it most directly reflects the main objective of this study: predicting fertility, +and we want to penalize larger errors more, as they are much more problematic for our use-case scenarios. +In cases where the difference between configurations was small (less than \(\pm\) 5\% metric value), we preferred the option that aligned with the general tendency of the model architecture. For example, if an architecture consistently performed better with more input data or longer sequences, but the Fertility-Overall MSE was only marginally better for a shorter window, we selected the longer window. diff --git a/thesis/sections/related_work.tex b/thesis/sections/related_work.tex index 9303f30..3597be3 100644 --- a/thesis/sections/related_work.tex +++ b/thesis/sections/related_work.tex @@ -4,6 +4,12 @@ \section{Related Work}\label{sec:related_work} +This section will introduce related work of both ovulation detection and ovulation prediction. +First, we'll introduce early work on the detection of the ovulation based on biomarkers. +Then, we'll show how others have used body temperature to predict ovulation and what their limitations are. +Lastly, we will take a closer look at related work that uses other biomarkers as base, or as an addition to the body +temperature for ovulation and fertility prediction. + A variety of approaches have historically been explored for ovulation detection and prediction, ranging from hormonal assays to physiological signal tracking. @@ -73,7 +79,8 @@ For menstruation prediction, the model detected 70.70\% of menstruation days in These results indicate that the model performed well in regular cycles but struggled with irregularity, particularly in detecting the fertile window. Complementing academic efforts, several commercial products have adopted temperature-based tracking, -such as \textit{Ava}~\cite{sl_ava_nodate}, \textit{Daysy}~\cite{electronics_zykluscomputer_nodate} or \textit{Trackle}~\cite{noauthor_trackle_nodate}. +such as \textit{Ava}~\cite{sl_ava_nodate}, \textit{Daysy}~\cite{electronics_zykluscomputer_nodate}, \textit{Trackle}~\cite{noauthor_trackle_nodate} +or \textit{Natural Cycles}~\cite{noauthor_natural_nodate}. However, these products typically rely on proprietary algorithms, and no peer-reviewed publications are available detailing their methodology or performance. This lack of transparency limits their scientific evaluation and comparability. In contrast, the present study provides an open and data-driven approach to ovulation prediction based on continuous temperature data, aiming to contribute reproducible evidence to the field. @@ -81,7 +88,7 @@ In contrast, the present study provides an open and data-driven approach to ovul \subsection{Other Physiological Signals}\label{subsec:other_physiolocical_signals} In addition to temperature, other physiological signals have been explored for ovulation and cycle phase prediction. -As early as \citeyear{moreno_temporal_1988}, researchers investigated ovulation prediction based on the electrical resistance of salivary and vaginal secretions~\cite{moreno_temporal_1988}. +As early as~\citeyear{moreno_temporal_1988}, researchers investigated ovulation prediction based on the electrical resistance of salivary and vaginal secretions~\cite{moreno_temporal_1988}. Their study analyzed 29 cycles from 11 women, with daily recordings of BBT, urinary LH, pelvic ultrasound, and ovulation predictor kit results. Participants were under the age of 35, had cycle lengths between 25 and 35 days, and had abstained from hormone therapies for at least two months prior to the study. @@ -92,7 +99,7 @@ These findings suggest that electrical resistance is a strong physiological mark A related modern implementation is the commercial product \textit{kegg}~\cite{noauthor_kegg_nodate}, which measures the electrical resistance of cervical mucus. The device uses an undisclosed algorithm to estimate fertility status based on these readings, although no peer-reviewed validation studies are currently available. -\citeauthor{masuda_machine_2025} developed a machine learning algorithm to classify phases of the menstrual cycle +In~\citeyear{masuda_machine_2025}, \citeauthor{masuda_machine_2025} developed a machine learning algorithm to classify phases of the menstrual cycle (follicular vs. luteal) based on sleeping heart rate, as recorded by a fitness tracker~\cite{masuda_machine_2025}. They used an XGBoost classifier for this binary task and additionally performed ovulation day prediction, although the details of this task were not fully specified. @@ -119,7 +126,7 @@ waking—they report classification accuracies between 0.843 and 0.864, dependin with very similar numbers for precision, recall, specificity and F1 score. Ovulation day prediction yielded an average absolute error between 3.6 and 4.1 days. -\paragraph{Summary:} +\subsection{Summary}\label{subsec:related_work_summary} While various physiological signals and modeling strategies have been explored for ovulation prediction, many existing studies are limited by small, highly selective datasets, assumptions of cycle regularity, or reliance on proprietary algorithms. The present work extends prior approaches by leveraging a large, heterogeneous dataset of real-world cycles and applying transparent, diff --git a/thesis/sections/results.tex b/thesis/sections/results.tex index 4a6e0a6..d76d81f 100644 --- a/thesis/sections/results.tex +++ b/thesis/sections/results.tex @@ -3,19 +3,18 @@ \section{Results}\label{sec:results} -We report results for two targets: (i) fertility probability and (ii) an indicator that ovulation has already occurred (OV-Over). -Unless stated otherwise, \textbf{MSE} is the primary metric (equivalent to the Brier score for probabilistic targets), -and \textbf{MAE} is secondary. -We first summarize overall performance across architectures, then analyze sensitivity to input window length, -input resolution, and model capacity. -Finally, we compare the best configurations to three baselines and present stratified and use-case analyses. -\subsection{Overall Model Performance Across Architectures}\label{subsec:overall_model_performance_across_architectures} +% find introduction + +\subsection{RQ1: Comparative Study of Model Architectures}\label{subsec:rq1_temp_predictive_value} We systematically evaluated multiple model architectures to assess their performance in -predicting fertility probability and ovulation-over targets. +predicting the targets. +We report results for two targets: (i) fertility probability and (ii) an indicator that ovulation has already occurred (OV-Over). Each architecture was tested across a range of input window lengths, temporal resolutions, and model capacities. The best-performing configurations for each architecture were selected for downstream analyses. +Unless stated otherwise, \textbf{MSE} is the primary metric (equivalent to the Brier score for probabilistic targets), +and \textbf{MAE} is secondary. All metrics reported in this section are point estimates without confidence intervals; therefore, comparisons are descriptive. @@ -124,7 +123,7 @@ Results are summarized in Table~\ref{tab:fertility_resolution_compact_mse}; full \paragraph{Impact of model parameters.} We next evaluate how architectural complexity, i.e., hidden size, number of layers, -and number of attention heads—influences performance under fixed input settings (160 days at 12/day for LSTM/Transformer, 40 days for convolutional models). +and number of attention heads, influences performance under fixed input settings (160 days at 12/day for LSTM/Transformer, 40 days for convolutional models). Parameters are reported as (hidden size × number of layers) for LSTM models and (embedding size × number of encoders × attention heads) for Transformers. For the \textbf{LSTM}, the best overall and fertile-day MSEs are both observed at a configuration with 512 hidden units and 4 layers. @@ -194,9 +193,6 @@ The \textbf{LSTM} performs best before ovulation (MSE 0.0212 at 20 days), while The \textbf{Convolutional LSTM} favors short windows, with the best overall MSE (0.0699), before-OV (0.0389), and after-OV (0.0833) all occurring at 20 days. The \textbf{Convolutional Transformer} performs best overall at 40 days and best before ovulation at 160 days (MSE 0.0286). - -These trends suggest that intermediate windows (20–40 days) often balance short- and long-term signal, -while long windows (e.g., 160 days) help capture post-ovulation patterns. See Table~\ref{tab:ovover_windows_compact_mse} for a summary. \begin{table}[t] @@ -242,7 +238,6 @@ with best before-OV and after-OV MSEs at 12/day (0.0212) and 48/day (0.0550), re The \textbf{Transformer} performs best overall at 72/day (0.0585), with lowest before-OV MSE at 12/day (0.0255) and after-OV MSE at 288/day (0.0578). -This indicates that high resolution benefits post-ovulation prediction, while lower rates suffice for pre-ovulation. Results are summarized in Table~\ref{tab:ovover_resolution_compact_mse}; full resolution grids are in Appendix Table~\ref{tab:ov_over_results_by_resolution}. @@ -334,7 +329,8 @@ while also considering the general performance trends of each model. Tables~\ref{tab:best_configs_lstm} and~\ref{tab:best_configs_transformer} summarize the selected hyperparameters for each architecture, including input window length, temporal resolution, and model complexity. For convolutional models, the input resolution was always fixed at the full 288 measurements per day. - +For the Transformer architecture, we made the decision to use longer input windows, as the performance on longer +input windows was competitive to the best measured at 40 days, but includes much more context information. \begin{table}[htbp] \centering @@ -389,9 +385,9 @@ All trained architectures outperform the baseline models across all evaluation m & Fertility Overall & Fertile Days & Non-Fertile Days & OV-Over Overall & OV-Over Before OV & OV-Over After OV \\ \midrule Transformer & 0.0037 & \textbf{0.0089} & 0.0017 & 0.0508 & 0.0236 & 0.0581 \\ - Convolutional Transformer & 0.0038 & 0.0098 & 0.0014 & 0.0517 & 0.0305 & 0.0566 \\ LSTM & \textbf{0.0036} & 0.0104 & \textbf{0.0008} & \textbf{0.0437} & \textbf{0.0233} & \textbf{0.0492} \\ Convolutional LSTM & 0.0037 & 0.0092 & 0.0014 & 0.0499 & 0.0281 & 0.0563 \\ + Convolutional Transformer & 0.0038 & 0.0098 & 0.0014 & 0.0517 & 0.0305 & 0.0566 \\ User-Based-Mean Baseline & 0.0064 & 0.0154 & 0.0028 & 0.1072 & 0.0872 & 0.0983 \\ Last-Cycle Baseline & 0.0080 & 0.0202 & 0.0031 & 0.1453 & 0.1099 & 0.1762 \\ Population-Mean Baseline & 0.0127 & 0.0258 & 0.0074 & 0.2145 & 0.0400 & 0.3749 \\ @@ -410,9 +406,89 @@ Compared to the strongest baseline (User-Mean), the LSTM reduces the overall fer The performance gap is even larger when compared to the population-mean and last-cycle baselines, confirming the advantage of personalized and temporally-aware modeling. -\subsection{Stratified Analysis}\label{subsec:stratified_analysis} +% ------------------------------------------------------------------------------------------------------------------------------- + +\subsection{RQ2: Factors and Patterns that Influence Predictions}\label{subsec:rq2_factors_and_patterns} + +\subsubsection{Representative predictions}\label{subsubsec:rq2_examples} + +All following prediction were made with the best model for each architecture as selected in the previous section. + +\begin{figure}[htbp] + \centering + \includegraphics[width=1.0\textwidth]{resources/figures/results/regular_cycle_pattern_fertility_prediction_example} + \caption{ + Temperature rolling average and fertility-probability prediction for a user with a regular cycle pattern. (Values are scaled features) + } + \label{fig:results_rq2_regular_cycle_predictions_example} +\end{figure} + +To visualize potential patterns in the predictions, we will show some representative prediction plots. + +All models have a similar predictive behaviour throughout both the regular and irregular cycles. +Figure~\ref{fig:results_rq2_regular_cycle_predictions_example} shows, that all models come very close to the ground truth +for a regular cycle pattern. +Slight deviations from the regular cycle length, such as in cycle 5 (Measurement 150--220) show, that the models +tend to overestimate the fertility-probability in such cases. +Additionally, all models never reach the full range of fertility-probability as indicated by the ground-truth. + +These predictions show a clear correlation with a temperature drop before the ovulation. + +\begin{figure}[htbp] + \centering + \includegraphics[width=1.0\textwidth]{resources/figures/results/temperature_drop_pattern} + \caption{ + Temperature rolling average and fertility-probability prediction showing a correlation between fertility and + the pre-ovulator temperature drop. + } + \label{fig:results_temperature_drop_pattern} +\end{figure} + +This correlation between a pre-ovulator temperature drop and the fertility can be seen more pronounced in Figure~\ref{fig:results_temperature_drop_pattern}. +The ground-truth fertility seems to be centered around this temperature drop and all models seem to be able +to pick it up, if it is clearly distinguishable and visible in the data. +For regular cycles, this temperature drop seems to be much easier distinguishable, +as there is not as much temperature variability throughout the first cycle phase. +Additionally, there also seems to be a correlation between the height of the temperature drop and the corresponding +fertility-probability prediction by the models. +The second cycle (Measurement 35--90) has a smaller temperature drop and all models predict a smaller fertility-probability +as for the following two cycles (Measurements 90--145). +This association of temperature-drop height and fertility-probability can also be seen in Figure~\ref{fig:results_rq2_regular_cycle_predictions_example}. +The first two cycles have a smaller temperature drop compared to the following cycles and also a less pronounced prediction. + +Figure~\ref{fig:results_rq2_irregular_cycle_predictions_example} shows an irregular cycle pattern +and the fertility-probability predictions for it. +For irregular cycles, all models show much more struggle in determining the ground-truth fertility. +The pre-ovulator temperature drop is far less distinguishable. +The fifth cycle (measurement 450--700) shows a much more gradual temperature drop and all model's predictions +are far off target. +The previous 2 cycles (cycle 3 and 4) are shorter and have a much more distinguishable temperature drop +and thus all models show indication for a detection of the fertile phase. + +\begin{figure}[htbp] + \centering + \includegraphics[width=1.0\textwidth]{resources/figures/results/irregular_cycle_pattern_fertility_prediction_example} + \caption{ + Temperature rolling average and fertility-probability prediction for a user with an irregular cycle pattern. (Values are scaled features) + } + \label{fig:results_rq2_irregular_cycle_predictions_example} +\end{figure} + +All models seem to be easily confused by anomalies during this characteristic temperature drop, even for short and regular cycles. +Figure~\ref{fig:results_rq2_anomaly_in_temperature_drop} shows a cycle with a clear anomaly during the pre-ovulatory drop in cycle 3 (Measurements 90--125). +This confuses all models into ending the fertile phase earlier, even though fertility is likely still elevated. + +\begin{figure}[htbp] + \centering + \includegraphics[width=1.0\textwidth]{resources/figures/results/spike_in_temperature_drop} + \caption{ + Temperature rolling average and fertility-probability prediction for a user with an anomaly during the characteristic temperature drop. (Values are scaled features) + } + \label{fig:results_rq2_anomaly_in_temperature_drop} +\end{figure} + +\subsubsection{Effect of user history depth}\label{subsubsec:rq2_history_depth} -\subsubsection{Influence of User History Depth}\label{subsubsec:influence_of_past_user_data} \begin{figure}[htbp] \centering \includegraphics[width=0.8\textwidth]{resources/figures/results/performance_on_different_historical_contexts} @@ -426,16 +502,22 @@ confirming the advantage of personalized and temporally-aware modeling. Figure~\ref{fig:results_performance_on_different_historical_context} shows the MSE for the fertility probability and OV-over targets as a function of the number of past cycles available per user, for all model architectures and baselines. -All four models improve on both metrics as the amount of historical data increases. +All four models improve slightly on both metrics as the amount of historical data increases. The LSTM-based models show a larger relative improvement with longer user history than the Transformer-based variants. -The User-Mean Baseline also improves substantially, with the Last-Cycle Baseline showing a smaller but still notable gain. -In contrast, the Population-Mean Baseline performs worse (i.e., MSE increases) as more historical cycles are included. + +All baselines seem produce worse prediction with larger historical contexts. +The last-cycle and population-mean baselines show the largest decrease in performance, +especially for the fertility-probability prediction target. +The user-mean baselines offers largely unchanged fertility prediction performance, +but worsens on the ov-over prediction with more historical cycles available. Across all models and baselines, the variability of results increases with greater history depth: results are more tightly clustered around the trend line with short histories, but show greater scatter for users with longer data records. -\subsubsection{Regular vs Irregular Cycles}\label{subsubsec:regular_vs_irregular_cycles} +% ------------------------------------------------------------- + +\subsection{RQ3: Performance across Regular and Irregular Cycles}\label{subsec:rq3_regular_vs_irregular} \begin{table}[htbp] \centering @@ -538,7 +620,6 @@ As before, all learned models outperform the baselines by a wide margin. The full table with MSE and MAE for all models can be found in the appendix, Table~\ref{tab:regular_vs_irregular_ov_over_results}. -\vspace{1em} \begin{figure}[htbp] \centering @@ -559,10 +640,12 @@ The full table with MSE and MAE for all models can be found in the appendix, Tab \end{figure} \paragraph{Evaluation on Historical Context Depth.} -Figures~\ref{fig:results_performance_on_regular_cycles} and~\ref{fig:results_performance_on_irregular_cycles} visualize performance improvements with increasing numbers of past cycles. +Figures~\ref{fig:results_performance_on_regular_cycles} and~\ref{fig:results_performance_on_irregular_cycles} +visualize performance improvements with increasing numbers of past cycles. For regular cycles, all models except the population-mean baseline benefit from more historical data, showing consistent MSE reductions for both targets. Interestingly, the population-mean baseline performs worse as more cycles are added. The largest improvement can be seen for the last-cycle baseline and user-mean baseline models. +The outliers in the last-cycle and user-mean baseline are MSE values of 0.0, which result in these vertical lines. In contrast, irregular cycles exhibit more variability and less performance gain with more cycles available. All trained models improve on both targets with more context. @@ -571,255 +654,242 @@ In contrast, their MSE increases with more historical context for the fertility- The last-cycle baseline shows marginal performance improvement for the OV-over target and the same increase for the fertility-probability target as the other two baselines. +The selected irregular cycle group has cycles with longer context, thus the irregular cycle history reaches up to 80 cycles, +while regular cycle history only reaches up to 35. + All models show increased output variability as the number of available past cycles grows, as indicated by the wider deviations from the trend lines. -\subsection{Use-Case Evaluation Results}\label{subsec:use_case_evaluation_results} +% ------------------------------------------------------------- + +\subsection{RQ4: Use-Case Evaluations}\label{subsec:rq4_use_case_evaluations} We evaluated the use-case scenarios described in Section~\ref{subsubsec:practical_use_cases} using the algorithms in Section~\ref{subsubsec:use_case_evaluation} across multiple fertility thresholds. Each evaluation used the same test set of 100 users (100 user-years) and was repeated for 200 iterations to get statistically more meaningful results. -we report means and 95\% confidence intervals (CIs). +All results represent the means over all runs. \subsubsection{Contraception Use-Case Results}\label{subsubsec:use_case_contraception_results} -\paragraph{Threshold 0.01 (Table~\ref{tab:results_contraception_use_case_0_01}).} -At the strictest threshold of 0.01, Transformer and LSTM models achieve the lowest pregnancy rates, only 4.4 to 4.6 -on average, despite allowing approximately 3,200 intercourse events. -This equates to about 1.4 pregnancies per 1,000 intercourse events, a strong result for contraceptive reliability. - -In contrast, convolutional variants (ConvLSTM and ConvTransformer) restrict intercourse events to around 1,150, -leading to roughly 34–35 pregnancies, or about 30 per 1,000 events—far less efficient in terms of balance between access and protection. -All baseline models perform substantially worse, with pregnancy counts exceeding 100 in all cases, confirming the value of personalized predictions. - -\begin{table} +\begin{figure}[htbp] \centering + \includegraphics[width=0.9\textwidth]{resources/figures/results/contraception_use_case_results_by_fertility_threshold} + \caption{ + Contraception use-case evaluation study results by fertility threshold. + } + \label{fig:results_contraception_use_case_results_by_fertility_threshold} +\end{figure} - \begin{subtable}{\textwidth} - \centering - \scriptsize - \begin{tabularx}{\linewidth}{l*{5}{X}} - \toprule - Model & No. of Intercourse Events & Pregnancies & Correct Denials & Incorrect Denials \\ - \midrule - Convolutional LSTM & 1154 (1149-1158) & 34.9 (34.1-35.7) & 1615 (1611-1620) & 4963 (4954-4971) \\ - Transformer & 3200 (3194-3207) & 4.4 (4.1-4.7) & 1817 (1812-1822) & 2717 (2710-2724) \\ - Convolutional Transformer & 1156 (1151-1160) & 34.3 (33.5-35.0) & 1613 (1608-1619) & 4974 (4965-4983) \\ - LSTM & 3206 (3199-3213) & 4.6 (4.3-4.9) & 1832 (1827-1838) & 2711 (2705-2717) \\ - Last-Cycle Baseline & 6066 (6057-6075) & 127.5 (125.9-129.1) & 918 (915-922) & 762 (759-766) \\ - Population-Mean Baseline & 5874 (5865-5883) & 153.7 (151.9-155.5) & 749 (746-752) & 1121 (1116-1125) \\ - User-Mean Baseline & 5933 (5924-5942) & 105.8 (104.4-107.2) & 1030 (1026-1034) & 776 (772-779) \\ - \bottomrule - \end{tabularx} - \caption{Contraception metrics at threshold \textbf{0.01} for all models.} - \label{tab:results_contraception_use_case_0_01} - \end{subtable} +Figure~\ref{fig:results_contraception_use_case_results_by_fertility_threshold} shows the outcomes of +the contraception use-case across fertility thresholds for each model architecture, along with the baselines. +The four evaluated metrics are: number of pregnancies, number of intercourse events, +number of correct denials (i.e., appropriately flagged fertile days), +and number of incorrect denials (i.e., fertile days incorrectly flagged as infertile). - \vspace{1.5em} +\paragraph{Pregnancies.} +All trained models start with very low pregnancy rates (Transformer: 0, LSTM: 4) +and then increase roughly linearly with the threshold, +reaching between 99 (Convolutional Transformer) and 122 (Transformer) at the highest setting. +The LSTM performs worst overall in this metric. +The Transformer starts with the lowest pregnancy rate but rises steeply, +ending with the highest number of pregnancies at threshold 0.1. +By comparison, the baselines show much higher pregnancy rates throughout, +starting between 103 (user-mean) and 150 (population-mean) and increasing by about 50 pregnancies over the range of thresholds. - \begin{subtable}{\textwidth} - \centering - \scriptsize - \begin{tabularx}{\linewidth}{l*{5}{X}} - \toprule - Model & No. of Intercourse Events & Pregnancies & Correct Denials & Incorrect Denials \\ - \midrule - Convolutional LSTM & 2509 (2502-2515) & 55.0 (54.0-56.0) & 1448 (1444-1452) & 3768 (3760-3775) \\ - Transformer & 3801 (3793-3810) & 8.2 (7.8-8.6) & 1768 (1763-1773) & 2162 (2157-2168) \\ - Convolutional Transformer & 2510 (2503-2516) & 55.6 (54.5-56.6) & 1448 (1443-1453) & 3776 (3769-3783) \\ - LSTM & 3801 (3794-3809) & 8.2 (7.8-8.7) & 1772 (1766-1777) & 2160 (2154-2165) \\ - Last-Cycle Baseline & 6106 (6096-6116) & 130.2 (128.7-131.7) & 888 (885-892) & 750 (746-753) \\ - Population-Mean Baseline & 5924 (5914-5933) & 156.1 (154.3-157.8) & 716 (713-720) & 1100 (1096-1104) \\ - User-Mean Baseline & 5988 (5978-5997) & 108.8 (107.3-110.3) & 998 (994-1002) & 764 (761-768) \\ - \bottomrule - \end{tabularx} - \caption{Contraception metrics at threshold \textbf{0.05} for all models.} - \label{tab:results_contraception_use_case_0_05} - \end{subtable} +\paragraph{Intercourse Events.} +All trained models except the LSTM begin with low values of about 1000 intercourse events. +The LSTM starts much higher, around 3000, and increases sharply, converging toward approximately 6100. +All trained model variants show curved growth resembling logarithmic convergence, approaching the same upper range. +In contrast, the baselines start near 6000 events and converge toward the same levels as the trained models at higher thresholds. - \vspace{1.5em} +\paragraph{Correct Denials.} +The number of correct denials decreases by about half across the tested thresholds. +All trained models cluster closely, starting at around 1800 correct denials, +with the Transformer and its convolutional variant slightly outperforming the LSTMs. +The baselines start at substantially lower levels: 1012 (user-mean), 902 (last-cycle), and 737 (population-mean). - \begin{subtable}{\textwidth} - \centering - \scriptsize - \begin{tabularx}{\linewidth}{l*{5}{X}} - \toprule - Model & No. of Intercourse Events & Pregnancies & Correct Denials & Incorrect Denials \\ - \midrule - Convolutional LSTM & 3596 (3588-3603) & 75.2 (74.0-76.4) & 1292 (1288-1297) & 2847 (2840-2853) \\ - Transformer & 4226 (4217-4234) & 13.3 (12.8-13.7) & 1692 (1687-1696) & 1822 (1817-1827) \\ - Convolutional Transformer & 3592 (3584-3599) & 73.8 (72.7-75.0) & 1296 (1292-1301) & 2846 (2840-2852) \\ - LSTM & 4222 (4214-4230) & 13.3 (12.8-13.8) & 1694 (1688-1699) & 1818 (1812-1823) \\ - Last-Cycle Baseline & 6145 (6135-6155) & 134.8 (133.3-136.3) & 865 (861-868) & 744 (740-747) \\ - Population-Mean Baseline & 5960 (5951-5969) & 160.3 (158.5-162.1) & 694 (691-697) & 1088 (1084-1093) \\ - User-Mean Baseline & 6014 (6005-6022) & 111.8 (110.3-113.3) & 969 (965-973) & 758 (755-762) \\ - \bottomrule - \end{tabularx} - \caption{Contraception metrics at threshold \textbf{0.10} for all models.} - \label{tab:results_contraception_use_case_0_10} - \end{subtable} +\paragraph{Incorrect Denials.} +For incorrect denials, the Transformer-based models (except LSTM) start at very high values near 5000 +but sharply decrease and converge between 500 and 1000. +The LSTM shows the same convergence pattern but starts considerably lower at 2654. +Baselines follow a different trend: the last-cycle and user-mean baselines decrease +modestly by about 100 over the range (starting at 749 and 762, respectively), +while the population-mean baseline instead decreases from 1102 to around 879. - \caption{grouped contraception metrics at thresholds 0.01, 0.05, and 0.10. values are means over 200 iterations; 95\% confidence intervals in parentheses.} - \label{tab:results_contraception_grouped} -\end{table} +\begin{figure}[htbp] + \centering + \includegraphics[width=0.9\textwidth]{resources/figures/results/contraception_use_case_pregnancy_statistics} + \caption{ + Contraception use-case results: pregnancies per 100 users per year and per 1000 intercourse events, stratified by fertility threshold. + } + \label{fig:results_contraception_use_case_pregnancy_statistics} +\end{figure} -\paragraph{Threshold 0.05 (Table~\ref{tab:results_contraception_use_case_0_05}).} -Increasing the threshold to 0.05 improves access: Transformer and LSTM models now permit approximately 3,800 intercourse -events while still limiting pregnancies to around 8.2 (2.2 per 1,000). -This represents a ~19\% increase in events over threshold 0.01, at the cost of a modest rise in pregnancies (+3.8 absolute, +86\% relative). +To facilitate comparison, Figure~\ref{fig:results_contraception_use_case_pregnancy_statistics} presents two +normalized pregnancy metrics across fertility thresholds: the number of pregnancies per 100 users per year (only one potential pregnancy is counted per user), +and the number of pregnancies per 1000 intercourse events. -Convolutional models also allow more events (~2,510) but continue to produce significantly more pregnancies (~55), -yielding a less favorable risk-benefit profile. -Baselines remain underperforming. +Here, the difference between the models becomes clearer. +The baselines start at 62 to 73 pregnancies per 100 users and become linearly worse over the threshold range. -\paragraph{Threshold 0.10 (Table~\ref{tab:results_contraception_use_case_0_10}).} -A further increase to 0.10 raises Transformer/LSTM events to ~4,220, but also raises pregnancies to ~13.3 (3.1 per 1,000). -This is a ~11\% gain in access compared to 0.05, but the pregnancy count increases by ~62\%. +The LSTM performs worse than all other trained models, except for the upper end of the threshold interval. +The Transformer, while being the best model for small thresholds, becomes worse over growing values until being on par with +the LSTM for the maximum tested value (0.1). -Meanwhile, convolutional models permit ~3,590 events and result in 74–75 pregnancies, indicating a consistent trade-off in favor of sequential models. - -\paragraph{Contraception—Recommended Threshold.} -Threshold 0.05 achieves a favorable balance between access and effectiveness. -Transformer and LSTM models perform best, allowing a relatively high number of intercourse events while keeping pregnancies low. -This threshold offers the best compromise and is selected as the most promising setting for contraceptive use. +\paragraph{Summary.} +In the contraception use-case, the trained models achieve substantially lower pregnancy rates than the baselines, +though at the cost of fewer intercourse opportunities and more denials, both correct and incorrect. +This results in less pregnancies for both 100 users for a year and per 1000 intercourse events for all trained models. +Within the trained models, Transformers and their convolutional variants perform best overall, while LSTMs lag behind across most metrics. +There is an edge for the non-convolutional models for smaller thresholds that switches for higher values. \subsubsection{Pregnancy Use-Case Results}\label{subsubsec:use_case_pregnancy_results} -\begin{table} +\begin{figure}[htbp] \centering + \includegraphics[width=0.9\textwidth]{resources/figures/results/pregnancy_use_case_results_by_fertility_threshold} + \caption{ + Contraception use-case evaluation study results by fertility threshold. + } + \label{fig:results_pregnancy_use_case_results_by_fertility_threshold} +\end{figure} - \begin{subtable}{\textwidth} - \centering - \scriptsize - \begin{tabularx}{\linewidth}{l*{5}{X}} - \toprule - Model & No. of Intercourse Events & Pregnancies & Correct Deferrals & Incorrect Deferrals \\ - \midrule - Convolutional LSTM & 4899 (4890-4908) & 216.9 (214.8-219.1) & 10038 & 1734 \\ - Transformer & 3685 (3678-3692) & 264.9 (262.7-267.0) & 17489 & 350 \\ - Convolutional Transformer & 4903 (4894-4912) & 214.3 (212.2-216.4) & 10038 & 1734 \\ - LSTM & 3688 (3680-3696) & 266.9 (264.7-269.1) & 17489 & 350 \\ - Last-Cycle Baseline & 1536 (1531-1541) & 138.7 (137.1-140.2) & 24593 & 4038 \\ - Population-Mean Baseline & 1704 (1699-1709) & 113.5 (112.1-114.9) & 22956 & 4841 \\ - User-Mean Baseline & 1652 (1647-1656) & 158.4 (156.8-160.0) & 24528 & 3523 \\ - \bottomrule - \end{tabularx} - \caption{Pregnancy metrics at threshold \textbf{0.01} for all models. } - \label{tab:results_pregnancy_use_case_0_01} - \end{subtable} - \vspace{1.5em} +Figure~\ref{fig:results_pregnancy_use_case_results_by_fertility_threshold} presents the results for the pregnancy use-case +across varying fertility thresholds, comparing all model architectures and baselines. +The four reported metrics are: number of pregnancies, number of intercourse events, number of correct deferrals (i.e., correctly flagged infertile days), +and number of incorrect deferrals (i.e., fertile days incorrectly flagged as infertile). - \begin{subtable}{\textwidth} - \centering - \scriptsize - \begin{tabularx}{\linewidth}{l*{5}{X}} - \toprule - Model & No. of Intercourse Events & Pregnancies & Correct Deferrals & Incorrect Deferrals \\ - \midrule - Convolutional LSTM & 6174 (6164-6184) & 236.1 (234.1-238.2) & 4332 & 1076 \\ - Transformer & 4253 (4245-4261) & 270.9 (268.6-273.1) & 14825 & 190 \\ - Convolutional Transformer & 6173 (6164-6182) & 235.7 (233.6-237.9) & 4332 & 1076 \\ - LSTM & 4255 (4247-4262) & 273.0 (270.6-275.3) & 14825 & 190 \\ - Last-Cycle Baseline & 1581 (1576-1586) & 139.8 (138.2-141.3) & 24327 & 4091 \\ - Population-Mean Baseline & 1752 (1747-1757) & 117.1 (115.6-118.6) & 22655 & 4888 \\ - User-Mean Baseline & 1695 (1690-1701) & 158.3 (156.6-160.1) & 24271 & 3567 \\ - \bottomrule - \end{tabularx} - \caption{Pregnancy metrics at threshold \textbf{0.05} for all models.} - \label{tab:results_pregnancy_use_case_0_05} - \end{subtable} +The overall pattern mirrors the contraception use-case, but the optimization goal is reversed: +here, a higher number of pregnancies is desirable. - \vspace{1.5em} +\paragraph{Pregnancies.} +Trained models achieve substantially higher pregnancy rates than the baselines. +They begin at around 275 pregnancies for low thresholds and decline linearly to between 157 and 176 at higher thresholds. +By contrast, the baselines start much lower (user-mean: 159, last-cycle: 139, population-mean: 117) and also decrease linearly, +reaching 119, 102, and 71 pregnancies, respectively. +This places all trained models well above baseline performance across the full threshold range. - \begin{subtable}{\textwidth} - \centering - \scriptsize - \begin{tabularx}{\linewidth}{l*{5}{X}} - \toprule - Model & No. of Intercourse Events & Pregnancies & Correct Deferrals & Incorrect Deferrals \\ - \midrule - Convolutional LSTM & 3887 (3880-3895) & 198.0 (196.1-199.9) & 14544 & 2296 \\ - Transformer & 3291 (3284-3298) & 260.7 (258.6-262.7) & 19261 & 551 \\ - Convolutional Transformer & 3883 (3875-3891) & 198.4 (196.6-200.3) & 14544 & 2296 \\ - LSTM & 3296 (3289-3303) & 261.1 (259.1-263.2) & 19261 & 551 \\ - Last-Cycle Baseline & 1509 (1504-1513) & 135.2 (133.6-136.8) & 24778 & 4010 \\ - Population-Mean Baseline & 1668 (1663-1673) & 109.5 (108.1-110.9) & 23156 & 4811 \\ - User-Mean Baseline & 1619 (1614-1624) & 154.0 (152.4-155.6) & 24717 & 3501 \\ - \bottomrule - \end{tabularx} - \caption{Pregnancy metrics at threshold \textbf{0.10} for all models.} - \label{tab:results_pregnancy_use_case_0_10} - \end{subtable} +\paragraph{Intercourse Events.} +Trained models start with high numbers of intercourse events, near 7000, except for the LSTM which begins lower at 4252. +All decline sublinearly and converge toward approximately 1500 at the highest thresholds. +In contrast, the baselines start far lower, between 1700 and 1900, and decrease linearly to about 1300. +Thus, the improved pregnancy rates of trained models come at the cost of substantially higher intercourse event counts. - \caption{grouped pregnancy metrics at thresholds 0.01, 0.05, and 0.10. values are means over 200 iterations; 95\% confidence intervals in parentheses. - Under our simulation at a fixed threshold, correct/incorrect deferrals are deterministic; CIs are therefore omitted for these columns.} - \label{tab:results_pregnancy_grouped} -\end{table} +\paragraph{Correct Deferrals.} +The baselines initially show higher numbers of correct deferrals, with values that increase linearly as thresholds rise. +Trained models begin much lower but follow a logarithmic-like growth pattern, +eventually converging with the baselines at high thresholds. +Within the trained group, the LSTM starts with noticeably higher values than the other models, though all converge toward a similar range. -\paragraph{Threshold 0.01 (Table~\ref{tab:results_pregnancy_use_case_0_01}).} -In the conception use-case, Transformer and LSTM models generate the highest pregnancy counts (~265–267) at ~3,685 intercourse events (72 per 1,000). -Convolutional models yield fewer pregnancies (~214–217) but allow ~4,900 intercourse events, resulting in ~44 pregnancies per 1,000. +\paragraph{Incorrect Deferrals.} +For trained models, incorrect deferrals start very low at small thresholds +(Transformer: 11, LSTM: 190) and grow with a curved, logarithmic-like pattern. +At high thresholds they reach between 1906 (Convolutional Transformer) and 2378 (Transformer). +The LSTM consistently produces more incorrect deferrals than the other trained models, except upper end of the threshold value range. +The Transformer once again shows the familiar pattern of excelling at low thresholds +but converging to the weakest performance among trained models at higher thresholds. -Baselines underperform on both metrics, allowing fewer events and achieving lower pregnancy counts, suggesting they are -overly conservative without yielding benefits in effectiveness. +\begin{figure}[htbp] + \centering + \includegraphics[width=0.9\textwidth]{resources/figures/results/pregnancy_use_case_pregnancy_statistics} + \caption{ + Contraception use-case evaluation study results by fertility threshold. + } + \label{fig:results_pregnancy_use_case_pregnancy_statistics} +\end{figure} -\paragraph{Threshold 0.05 (Table~\ref{tab:results_pregnancy_use_case_0_05}).} -At threshold 0.05, Transformer/LSTM models slightly increase intercourse access (~4,250 events) with pregnancies rising to ~271–273 (64 per 1,000). -Notably, these models also achieve very low incorrect deferral counts (~190), indicating they rarely block opportunities for conception when they shouldn’t. +To facilitate comparison here as well, Figure~\ref{fig:results_pregnancy_use_case_pregnancy_statistics} presents two +normalized pregnancy metrics across fertility thresholds: the number of pregnancies per 100 users per year, +and the number of pregnancies per 1000 intercourse events. -Convolutional models allow substantially more intercourse (~6,170) with lower pregnancy counts (~236), -but at the cost of higher incorrect deferrals (~1,076). -This suggests they are more permissive but less selective. +All trained models clearly result in more pregnancies per 100 users overall. +However, for lower fertility thresholds, there are fewer pregnancies per 1000 intercourse events for the trained models +compared to the baselines. -\paragraph{Threshold 0.10 (Table~\ref{tab:results_pregnancy_use_case_0_10}).} -At the highest threshold, Transformer/LSTM models see a drop in access (~3,290 events) and in pregnancies (~261), -but with an increase in incorrect deferrals (~551). -Convolutional models again show a more permissive profile (~3,880 events, ~198 pregnancies), but with higher rates of incorrect deferrals (~2,296). +\paragraph{Summary.} +In the pregnancy use-case, trained models clearly outperform baselines in terms of pregnancy rates, +though at the expense of more intercourse events and higher incorrect deferrals. +This results in less pregnancies per 1000 intercourse events for the trained models on smaller thresholds. +However, for 100 user-years, the pregnancy rates are noticeably higher than the baselines. +The LSTM underperforms relative to the Transformer-based architectures across most metrics, +while the Transformer itself exhibits strong performance at low thresholds but deteriorates more rapidly with increasing thresholds. +There is an edge for the non-convolutional models for smaller thresholds that switches for higher values. -This threshold leads to fewer pregnancies and more unnecessary blocks, especially for the Transformer/LSTM models. -\paragraph{Pregnancy—Recommended Threshold.} -Threshold 0.05 strikes the best balance for conception as well. -Transformer and LSTM models provide high pregnancy counts and good access with minimal incorrect deferrals. -Compared to 0.10, it results in more successful conceptions with fewer missed opportunities, making it the optimal setting for this use-case. +% ------------------------------------------------------------- -\subsection{Summary of Key Findings}\label{subsec:summary_of_key_findings} +\subsection{Summary of key results}\label{subsec:key_results} +% Keep this as a tight bulleted list mirroring RQs; strictly findings, no causes/interpretations. -\paragraph{Model Performance.} -LSTM models consistently achieved the lowest overall mean squared errors (MSE) across both prediction targets, -fertility probability and ovulation-over (OV-over), on the held-out test set. -Transformer models slightly outperformed LSTMs on fertile-day predictions, suggesting higher sensitivity to short-term signals. -Convolutional models (ConvLSTM and ConvTransformer) performed competitively in some configurations but generally -exhibited higher error and less favorable trade-offs in practical scenarios. +%\begin{itemize}[leftmargin=*] +% \item \textbf{RQ1:} Temperature-based models achieve lowest MSEs on regular cycles; errors increase on irregular cycles. +% More history generally reduces MSE, with greater variance at higher history depth. +% \item \textbf{RQ2:} In contraception and conception scenarios, sequential models (LSTM/Transformer) yield +% the most favorable access–outcome trade-offs at threshold 0.05. +% \item \textbf{RQ3:} All ML models outperform rule-based baselines across targets and splits; +% the largest margins occur on post-ovulation metrics. +%\end{itemize} -\paragraph{Effect of Input Settings.} -\textit{Input window length} significantly influenced performance. -LSTMs and Transformers benefited from longer windows (e.g., 160\,days), especially for post-ovulation detection, -while convolutional models favored shorter windows (e.g., 20--40\,days). -\textit{Input resolution} showed that intermediate sampling rates (4--48\,values/day) often minimized overall error, -though full resolution (288/day) was most useful for detecting fertile windows in LSTM models. +\noindent\textbf{RQ1 (Architectures, context, and capacity).} +\begin{itemize} + \item \emph{Architectures.} All learned models beat baselines by a wide margin. + The \textbf{LSTM} is the most reliable overall and post-ovulation; the \textbf{Transformer} is strongest on fertile days. + Convolutional variants are competitive but rarely best. -\paragraph{Model Scaling.} -Larger configurations (e.g., 512 hidden units, 4--8 layers, 4--8 attention heads) generally improved performance across architectures, -especially for Transformers in OV-over prediction. + \item \emph{Temporal context.} Performance depends on window length and differs by model. + LSTM benefits from long context (up to 160\,days), Transformers peak around 40–80\,days; conv models prefer mid-range (40\,days). + (Tables~\ref{tab:fertility_windows_compact_mse},~\ref{tab:ovover_windows_compact_mse}.) -\paragraph{Comparison to Baselines.} -All trained models substantially outperformed baseline predictors (user mean, last-cycle, population mean) across metrics and targets. -The LSTM model reduced fertility MSE by 44\% and post-ovulation MSE by 59\% compared to the strongest baseline. + \item \emph{Input resolution.} Low–medium rates (4–48/day) minimize \emph{overall} error for sequence models; high rates help fertile-day signals. + Conv models learn their own compression from full resolution. (Tables~\ref{tab:fertility_resolution_compact_mse},~\ref{tab:ovover_resolution_compact_mse}.) -\paragraph{Stratified Analyses.} -\textit{User history depth:} More historical cycles consistently improved prediction accuracy, especially for LSTM models. -However, output variability increased with longer histories. -\textit{Cycle regularity:} All models performed better on regular cycles than irregular ones. -LSTM remained the most robust across both groups. + \item \emph{Capacity.} Moderate-to-large configurations improve accuracy but with target- and phase-specific optima + (e.g., LSTM \(512\times4\); Transformer \(512\times4\times4\) for fertility overall, \(512\times8\times8\) for OV-over after-OV). + (Tables~\ref{tab:fertility_params_compact_mse}, \ref{tab:ovover_params_compact_mse}.) -\paragraph{Use-Case Scenarios.} -In \textit{contraceptive settings}, Transformer and LSTM models maintained pregnancy rates below 2.2 per 1{,}000 intercourse events -at a 0.05 threshold---demonstrating both high reliability and user access. -In \textit{conception settings}, the same models yielded the highest pregnancy rates ($\sim$64 per 1{,}000 events) -with minimal missed opportunities, again at the 0.05 threshold. -Convolutional models allowed more events but were less selective, -resulting in higher pregnancy rates in contraception and lower in conception scenarios. -Baseline methods were consistently outperformed in both use-cases, often sacrificing either effectiveness or access. \ No newline at end of file + \item Best configs (Tables~\ref{tab:best_configs_lstm},~\ref{tab:best_configs_transformer}) + generalize on the held-out test set (Table~\ref{tab:results_model_selection_metrics}). + + \item LSTM is best \emph{overall}: fertility MSE \(0.0036\) (best overall; best non-fertile \(0.0008\)) and OV-over overall \(0.0437\) + (best; best after-OV \(0.0492\)). Transformer is best on fertile days (MSE \(0.0089\)). + + \item Versus the strongest baseline (User-Mean), LSTM cuts fertility MSE by \(\sim 44\%\) and post-ovulatory MSE by \(\sim 59\%\). +\end{itemize} + +\noindent\textbf{RQ2 (Factors and Patterns).} +\begin{itemize} + \item Models consistently key on the \emph{pre-ovulatory temperature drop}; larger drops yield higher predicted fertility. + This pattern weakens in irregular cycles with noisier temperature traces (Figures~\ref{fig:results_rq2_regular_cycle_predictions_example}–\ref{fig:results_rq2_irregular_cycle_predictions_example}, \ref{fig:results_temperature_drop_pattern}). + + \item More user history helps modestly; LSTM variants benefit most. + Baselines do not: Last-Cycle and Population-Mean often degrade with more history (Figure~\ref{fig:results_performance_on_different_historical_context}). +\end{itemize} + +\noindent\textbf{RQ3 (Regular vs.\ Irregular).} +\begin{itemize} + \item All models are better on \emph{regular} cycles. + For fertility, Transformer leads overall and on fertile days; LSTM leads on non-fertile days. + For OV-over, LSTM leads overall and after-OV; Transformer leads before-OV (Tables~\ref{tab:fertility_mse_regular_irregular},~\ref{tab:ov_over_mse_regular_irregular}). + + \item In \emph{irregular} cycles, errors rise across the board. + LSTM remains most robust; Convolutional LSTM is competitive for fertility overall; Transformers are less stable before/after phase splits. + + \item Variance of predictions grow with longer context lengths. +\end{itemize} + +\noindent\textbf{RQ4 (Use-case Evaluations).} +\begin{itemize} + \item \emph{Contraception:} Trained models cut pregnancies sharply vs baselines, + at the cost of more denials and fewer intercourse opportunities. + Transformer variants are best at low thresholds; LSTM lags overall. + Convolutional models excel at higher thresholds + (Figures~\ref{fig:results_contraception_use_case_results_by_fertility_threshold},~\ref{fig:results_contraception_use_case_pregnancy_statistics}). + + \item \emph{Pregnancy seeking:} Trained models yield many more pregnancies than baselines but require + more intercourse events and accept more incorrect deferrals at high thresholds. + Transformer excels at low thresholds; LSTM underperforms overall. + Convolutional models again excel at higher thresholds + (Figures~\ref{fig:results_pregnancy_use_case_results_by_fertility_threshold},~\ref{fig:results_pregnancy_use_case_pregnancy_statistics}). +\end{itemize} \ No newline at end of file