started methodology

thesis/sections/methodology.tex

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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
 % 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}