started methodology

thesis/sections/background.tex

@@ -41,11 +41,24 @@ throughout the menstrual cycle.
 
 \begin{figure}
     \centering
-    \includegraphics[width=0.7\textwidth]{background_menstrual_cycle_physiology}
+    \includegraphics[width=0.6\textwidth]{background_menstrual_cycle_physiology}
     \caption{Physiological changes during the menstrual cycle\cite{pedroso_menstrual_2022}.}
     \label{fig:background_menstrual_cycle_physiology}
 \end{figure}
 
+Not every cycle results in ovulation—a phenomenon known as anovulation—which leads to a monophasic temperature pattern.
+Anovulation can have various causes, including hormonal imbalances, stress, or underlying health conditions\cite{rosenfield_adolescent_2013}.
+
+Anovulation is reflected in temperature data as either an absence of a clear temperature rise or a rise
+that is insufficient in magnitude or duration to be considered a reliable indicator of ovulation.
+Distinguishing between ovulatory and anovulatory cycles is challenging, as the only definitive confirmation of
+successful ovulation in a clinical sense is a positive pregnancy test.
+Even ultrasound imaging can only confirm that an egg was released from its follicle—not whether it was fertilized or successfully implanted.
+
+%TODO: find source for this
+
+%TODO: show plot of different cycle types
+
 \subsubsection{Fertility Prediction}\label{subsec:fertility_prediction}
 Throughout the menstrual cycle, the chance of fertilization varies significantly.
 An egg cell released from the ovary during ovulation, can be fertilized for up to 24 hours.
@@ -56,7 +69,7 @@ as illustrated in Figure~\ref{fig:background_pregnancy_chance}.
 
 \begin{figure}
     \centering
-    \includegraphics[width=0.8\textwidth]{background_pregnancy_chance_over_time}
+    \includegraphics[width=0.7\textwidth]{background_pregnancy_chance_over_time}
     \caption{Chance of fertilization depending on the day of the menstrual cycle.
     The highest chance is around one day before ovulation\cite{dunson_day-specific_1999}.}
     \label{fig:background_pregnancy_chance}
@@ -93,4 +106,5 @@ There are usually two main goals:
 understanding the underlying mechanisms that lead to the observed data and predicting future data points based on the
 historical information and potentially external factors\cite{cryer_time_2008}
 \\
-Time series analysis encompasses various methods, ranging from simple statistical models to complex deep learning architectures.
+Time series analysis encompasses various methods, ranging from simple statistical models to complex deep learning architectures.
+Classical methods

thesis/sections/methodology.tex

@@ -5,4 +5,49 @@
 
 % why did I select tft over other methods -> include examples of time series and why I belief a complex model could help
 % you did I apply it
-% implementation details
+% implementation details
+
+
+\subsection{Data Collection \& Preprocessing}\label{subsec:data_collection_preprocessing}
+\subsubsection{Data Collection}\label{subsubsec:data_collection}
+The dataset used in this work was collected by \textit{VivoSensMedical GmbH}, a medical technology company based in Leipzig, Germany,
+specializing in fertility tracking devices and applications.
+Each entry in the dataset is derived from temperature measurements recorded by the \textit{OvulaRing} wearable
+device\cite{alexander_fertilitatsmonitoring_2014}, which continuously measures core body temperature every 5 minutes.
+The sensor is worn intra-vaginally and is designed for extended use, requiring removal only for data synchronization.
+This enables high-resolution, long-term temperature tracking, providing a detailed view of menstrual cycle patterns.
+
+% add a few example cycles
+% TODO: stats about dataset
+The dataset used for training and evaluation consists of 45000 cycles,
+
+In addition to raw temperature values, the dataset includes user-specific metadata, such as health status, weight, and age.
+Users can also manually input time-dependent markers, indicating events such as menstruation, ovulation, and intercourse,
+which serve as contextual features for the model.
+While these additional data points can enhance predictive accuracy, it is important to note that they are self-reported
+and may be subject to errors or biases.
+
+\subsubsection{Data Preprocessing}\label{subsubsec:data_preprocessing}
+Not all cycles in the dataset can be used for training.
+Cycles that are too short are excluded, as they usually do not contain enough information to make reliable predictions.
+Additionally, cycles that are too long are also excluded, as they might contain multiple ovulation events or
+pregnancies, which would make the prediction task ambiguous.
+\subsubsection{Feature Engineering}\label{subsubsec:feature_engineering}
+
+%TODO: show that a model without marker context and one with them is trained, to show the impact of the markers
+
+
+\subsection{Time-Series Modeling Approach}\label{subsec:time-series_modeling_approach}
+\subsubsection{Limitations of Traditional Time-Series Models}\label{subsubsec:limitations_of_traditional_time-series_models}
+\subsubsection{Temporal Fusion Transformer}\label{subsubsec:temporal_fusion_transformer}
+\subsubsection{Input \& Output Modeling}\label{subsubsec:input_output_modeling}
+
+\subsection{Model Training}\label{subsec:model_training}
+\subsubsection{Training Setup}\label{subsubsec:training_setup}
+\subsubsection{Hyperparameter Tuning}\label{subsubsec:hyperparameter_tuning}
+\subsubsection{Training Details}\label{subsubsec:training_details}
+
+\subsection{Evaluation}\label{subsec:evaluation}
+\subsubsection{Evaluation Metrics}\label{subsubsec:evaluation_metrics}
+\subsubsection{Baseline Comparisons}\label{subsubsec:baseline_comparisons}
+\subsubsection{Explainability \& Interpretability}\label{subsubsec:explainability_interpretability}