Alex Blank
76 lines
4.9 KiB
TeX
76 lines
4.9 KiB
TeX
%! Author = alex
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%! Date = 3/6/25
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\section{Methodology}\label{sec:methodology}
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% why did I select tft over other methods -> include examples of time series and why I belief a complex model could help
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% you did I apply it
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% implementation details
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\subsection{Data Collection \& Preprocessing}\label{subsec:data_collection_preprocessing}
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\subsubsection{Data Collection}\label{subsubsec:data_collection}
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The dataset used in this work was collected by \textit{VivoSensMedical GmbH}, a medical technology company based in Leipzig, Germany,
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specializing in fertility tracking devices and applications.
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Each entry in the dataset is derived from temperature measurements recorded by the \textit{OvulaRing} wearable
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device\cite{alexander_fertilitatsmonitoring_2014}, which continuously measures core body temperature every 5 minutes.
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The sensor is worn intra-vaginally and is designed for extended use, requiring removal only for data synchronization.
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This enables high-resolution, long-term temperature tracking, providing a detailed view of menstrual cycle patterns.
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% add a few example cycles
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% TODO: stats about dataset
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The dataset used for training and evaluation consists of 45000 cycles,
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In addition to raw temperature values, the dataset includes user-specific metadata, such as health status, weight, and age.
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Users can also manually input time-dependent markers, indicating events such as menstruation, ovulation, and intercourse,
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which serve as contextual features for the model.
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While these additional data points can enhance predictive accuracy, it is important to note that they are self-reported
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and may be subject to errors or biases.
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\subsubsection{Data Preprocessing}\label{subsubsec:data_preprocessing}
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Not all cycles in the dataset can be used for training.
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Cycles that are too short are excluded, as they usually do not contain enough information to make reliable predictions.
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Additionally, cycles that are too long are also excluded, as they might contain multiple ovulation events or
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pregnancies, which would make the prediction task ambiguous.
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\subsubsection{Feature Engineering}\label{subsubsec:feature_engineering}
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%TODO: show that a model without marker context and one with them is trained, to show the impact of the markers
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%TODO: show that for initial testing and fine tuning the dataset was reduced to 10% of size to speed up early impressions of performance
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%TODO: show that initial feature set with only temperature as observable and only pregnancy "chance" as target
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% did not show promise on small 10% dataset, so the features got extended
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% show the modifications that were made to account for multi target prediction
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% show, the mothod of circumventing the non-padding-implementation by using a dedicated feature
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% show, that a different loss / temporal loss wheighting or position aware loss could help
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% show, the problem of the model using its past targets, even though they are not observable and how I dealt with this -> explain and use exogenous
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% introduce change of quantiles to single variable output and why, quantiles loss other than .5 was almost always just 0 or 1, so no significant predicitve quality in this, and this only makes predictions harder for model
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% explain why class imablances are there, and what I did to counteract this, also explain, why special weighting is useful for something like the pregnancy risk prediction
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% show the work that needed to be done for inference -> padding values for future observables
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Notes for training:
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- one head 512 hidden size make very "smoothed" out curves, which result in ver conservative predictions
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Intermediate training results:
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- first full training:
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- first feature config with ov over and fertility did not show much promise, as it detected the ov too late, essentially when it was already over
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- either there is no patterns detectable in advance, or there are not enough features yet
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- soft of "soft ceiling" at 0.3 loss, many models get there quickly and then "bounce" around
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\subsection{Time-Series Modeling Approach}\label{subsec:time-series_modeling_approach}
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\subsubsection{Limitations of Traditional Time-Series Models}\label{subsubsec:limitations_of_traditional_time-series_models}
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\subsubsection{Temporal Fusion Transformer}\label{subsubsec:temporal_fusion_transformer}
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\subsubsection{Input \& Output Modeling}\label{subsubsec:input_output_modeling}
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% build in the probability curve for the pregnancy chance
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\subsection{Model Training}\label{subsec:model_training}
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\subsubsection{Training Setup}\label{subsubsec:training_setup}
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\subsubsection{Hyperparameter Tuning}\label{subsubsec:hyperparameter_tuning}
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\subsubsection{Training Details}\label{subsubsec:training_details}
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\subsection{Evaluation}\label{subsec:evaluation}
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\subsubsection{Evaluation Metrics}\label{subsubsec:evaluation_metrics}
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\subsubsection{Baseline Comparisons}\label{subsubsec:baseline_comparisons}
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\subsubsection{Explainability \& Interpretability}\label{subsubsec:explainability_interpretability}
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