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 %! Author = alex
 %! Date = 3/6/25
 
+
 \section{Results}\label{sec:results}
 
-% provide information about the training behaviour and statistic of the different models
\ No newline at end of file
+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}