%! Author = alex
%! Date = 3/6/25


\section{Results}\label{sec:results}

The previous section detailed the design and implementation of our ovulation prediction pipeline,
including data preprocessing, feature engineering, input encoding, and the development of several deep learning architectures.
We now present the results of our evaluation, focusing on the predictive accuracy of the proposed models across different temporal resolutions,
cycle types, and user histories.
Model performance is assessed using both overall metrics and biologically targeted subintervals,
allowing for a nuanced comparison of approaches and their practical relevance to real-time fertility forecasting.

% provide information about the training behaviour and statistic of the different models

\subsection{Overall Model Performance Across Architectures}\label{subsec:overall_model_performance_across_architectures}

\subsection{Fertility Probability Prediction Accuracy}\label{subsec:fertility_probability_precition_accuracy}

\subsubsection{Performance Across Fertile Window}\label{subsubsec:fert_performance_across_fertile_window}

\subsubsection{Impact of Input Resolution}\label{subsubsec:fert_impact_of_input_resolution}

\subsubsection{Impact of Input Window Length}\label{subsubsec:fert_impact_of_historical_context}

\subsubsection{Comparison with Baselines}\label{subsubsec:fert_comparison_with_baselines}

\subsection{Ovulation-Over Prediction Accuracy}\label{subsec:ov_over_prediction_accuracy}

\subsubsection{Performance around Ovulation}\label{subsubsec:ov_over_performance_around_ovulation}

\subsubsection{Impact of Input Resolution}\label{subsubsec:ov_over_impact_of_input_resolution}

\subsubsection{Impact of Input Window Length}\label{subsubsec:ov_over_impact_of_historical_context}

\subsubsection{Comparison with Baselines}\label{subsubsec:ov_over_comparison_with_baselines}

\subsection{Stratified Analysis}\label{subsec:stratified_analysis}

\subsubsection{Regular vs Irregular Cycles}\label{subsubsec:regular_vs_irregular_cycles}

\subsubsection{Influence of User History Depth}\label{subsubsec:influence_of_past_user_data}

% show, that past cycles might not directly be included but are indirectly included by the static features

\subsection{Summary of Key Findings}\label{subsec:summary_of_key_findings}
