further work and refinements

thesis/sections/discussion.tex

@@ -20,9 +20,44 @@ We think, that for regular cycles, the performance difference will be lower than
 % explain the need for further medical interpretation of the results of either model
 
 
-\section{Future Work}\label{sec:future_work}
+The findings do not show a clear indication, that the temperature can be used as a predictive target.
+The results of irregular cycles should be significantly better to infer, that there are usable patters in the
+temperature readings before the ovulation or the fertile days.
 
-% inclusion of markers
-% better labeling process
-% more advanced models
-%
+For a practical use case of any model's prediction, a medical interpretation should be performed.
+While the results themselves can give a clear indication of both the fertility probability and whether the ovulation
+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.
+
+
+
+\section{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.
+
+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.
+
+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.