further work on methodology

thesis/main.tex

@@ -25,25 +25,25 @@
 
         \includegraphics[width=7cm]{leipzig_university_logo}\\[1cm] % Adjust size as needed
 
-        {\huge \textbf{Finding Predictors for Human Ovulation with Attention Mechanisms}}\\[1.5cm]
+        {\huge \textbf{Ovulation Prediction with Machine Learning}}\\[1.5cm]
 
         \textbf{Master’s Thesis}\\[1cm]
 
         \textbf{Author:}\\
         Alexander Blank\\[0.5cm]
 
-        \textbf{Supervisor(s):}\\
-        Prof. XYZ, Dr. ABC\\[1.5cm]
+        \textbf{Supervisor:}\\
+        Prof. Bogdan Franczyk\\[1.5cm]
 
-        \textbf{Date:} 2025\\[2cm]
+        \textbf{Date:} September 2025\\[2cm]
 
-        \textbf{Department of XYZ}\\
+        \textbf{Master of Science: Data Science}\\
         \textbf{Leipzig University}
     \end{titlepage}
 
     \pagebreak
     \begin{abstract}
-        This paper investigates the use of attention mechanisms to predict human ovulation. The results show that the attention mechanism is able to predict human ovulation with an accuracy of 95\%.
+        This could be your abstract - call 0800 - ABSTRACT to get your personal quote.
     \end{abstract}
     \pagebreak
 

thesis/sections/background.tex

@@ -48,12 +48,12 @@ However, variations, particularly in the follicular phase length, are common and
 Figure~\ref{fig:background_menstrual_cycle_physiology} provides a detailed overview of the hormonal and physiological changes
 throughout the menstrual cycle.
 
-\begin{figure}[htb]
-    \centering
-    \includegraphics[width=0.9\textwidth]{background_labeled_cycle}
-    \caption{A cycles temperature curve with its phases and ovulation}
-    \label{fig:background_labeled_cycle}
-\end{figure}
+%\begin{figure}[htb]
+%    \centering
+%    \includegraphics[width=0.9\textwidth]{background_labeled_cycle}
+%    \caption{A cycles temperature curve with its phases and ovulation}
+%    \label{fig:background_labeled_cycle}
+%\end{figure}
 
 Figure~\ref{fig:background_labeled_cycle} shows the temperature curve over the course of a menstrual cycle with the
 menstruation, fertile phase and ovulation marked.

thesis/sections/discussion.tex

@@ -8,6 +8,7 @@
 % talk about whether bbt / temperature can be used for such a task, discuss bbt doubt papers
 % While previous work has argued against the predictive value of BBT~\cite{some_author_2010}, our findings suggest otherwise.
 %Using continuous core body temperature data from 40,000 cycles, we demonstrate that temperature-based models can reliably detect ovulatory patterns, even in the presence of physiological noise or mild irregularity.
+% explain the need for further medical interpretation of the results of either model
 
 
 \section{Future Work}\label{sec:future_work}

thesis/sections/methodology.tex

@@ -125,12 +125,12 @@ These help the model place each observation in temporal context:
 
 Except for \textit{time since cycle start}, all features are encoded using sine and cosine transforms to preserve their cyclical nature and make them more interpretable for the model.
 
-\begin{figure}[htbp]
-    \centering
-    \includegraphics[width=0.9\textwidth]{methodology_time_feature_sine_encoded}
-    \caption{Sine and cosine encoding of the day-of-week feature.}
-    \label{fig:methodology_time_feature_encoding}
-\end{figure}
+%\begin{figure}[htbp]
+%    \centering
+%    \includegraphics[width=0.9\textwidth]{methodology_time_feature_sine_encoded}
+%    \caption{Sine and cosine encoding of the day-of-week feature.}
+%    \label{fig:methodology_time_feature_encoding}
+%\end{figure}
 
 Figure~\ref{fig:methodology_time_feature_encoding} illustrates the sine and cosine encoding of the day-of-week feature.
 The cyclical nature of the variable is clearly visible in the transformation.
@@ -144,16 +144,53 @@ Including this information could therefore improve the predictive quality of the
 
 \subsubsection{Observable Features}\label{subsubsec:observable_features}
 
-The observable features constitute the input features that are directly observable, but only until the current moment.
+Observable features include all time-series inputs available up to the current time step.
+They represent real-time physiological signals from which the model must infer ovulatory status.
 
 \begin{itemize}
-    \item \textbf{Temperature} - the raw temperature as recorded by the OvulaRing sensor
-    \item \textbf{Rolling Average Temperature} - the rolling average of the temperature over 1 day (288 measurements)
-    \item \textbf{Rolling Window Temperature Minimum} - the minimum temperature over a rolling window of 1 day
-    \item \textbf{Rolling Window Temperature Maximum} - the maximum temperature over a rolling window of 1 day
+    \item \textbf{Temperature} — Raw intravaginal temperature as recorded by the OvulaRing sensor, sampled every 5 minutes.
+    \item \textbf{Rolling Average Temperature} — The mean temperature over a 1-day (288-sample) sliding window, linearly interpolated to preserve the original input resolution.
+    \item \textbf{Rolling Window Temperature Minimum} — The minimum temperature observed within a 1-day window, highlighting potential overnight lows or phase-specific dips.
+    \item \textbf{Rolling Window Temperature Maximum} — The maximum temperature within a 1-day window, capturing transient peaks or elevated plateaus.
 \end{itemize}
 
-% table with all features
+These derived features are intended to reduce model complexity by providing smoothed or extremal summaries of the raw signal.
+The \textit{rolling average} allows the model to capture broader trends without having to learn temporal aggregation from scratch.
+The \textit{rolling minimum} and \textit{maximum} support the detection of boundary behavior (e.g., temperature shifts, sustained elevation, extreme values)
+without requiring explicit memory or aggregation.
+
+The 1-day window length reflects the expected circadian cycle and strikes a balance between temporal sensitivity and signal stability.
+
+\subsubsection{Target Features}
+
+Target features represent the outputs that the models are trained to predict.
+These can include exogenous biological outcomes or interpretable derivatives of input features.
+
+\begin{itemize}
+    \item \textbf{Fertility / Pregnancy Probability} — The estimated probability of conception from unprotected intercourse on the current day.
+    \item \textbf{Ovulation-Over Indicator} — A binary variable indicating whether ovulation has already occurred in the current cycle.
+\end{itemize}
+
+\begin{figure}[htbp]
+    \centering
+    \includegraphics[width=0.9\textwidth]{methodology_target_features}
+    \caption{
+        Target features plotted for a single cycle.
+        The ovulation-over indicator switches on the day of ovulation;
+        the fertility probability follows a curve based on known day-specific fecundability~\cite{dunson_day-specific_1999}.
+    }
+    \label{fig:methodology_target_features}
+\end{figure}
+
+The combination of these two targets is intended to provide the user with both physiological and practical insight:
+\textit{Fertility probability} conveys the likelihood of conception, but alone does not indicate whether ovulation is yet to come or has already passed.
+A fertility probability near zero could mean that ovulation is either in the past or still ahead—information the model alone cannot disambiguate.
+
+The \textit{ovulation-over indicator}, by contrast, explicitly marks the post-ovulatory phase, but does not describe conception risk.
+Together, the two outputs offer complementary information and improve interpretability for real-time user-facing applications.
+
+As discussed in Section~\ref{sec:discussion}, all predictions are subject to further interpretation before presentation in the product interface.
+The model outputs represent data-driven estimates and do not constitute medical advice or diagnostic statements.
 
 \subsection{Time-Series Modeling Approach}\label{subsec:time-series_modeling_approach}
 
@@ -161,6 +198,51 @@ The observable features constitute the input features that are directly observab
 
 % build in the probability curve for the pregnancy chance
 
+\subsubsection{Sequence Representation and Sampling}
+\label{subsubsec:input_sequence_construction}
+
+Due to the high temporal resolution of the temperature data (288 measurements per day), raw input sequences can become prohibitively long for most model types.
+
+To manage input size and evaluate the impact of temporal resolution on predictive performance, a parameterized resampling strategy is applied.
+Consecutive time steps are aggregated into bins of configurable size, and each bin is reduced to a single value using a feature-specific aggregation function.
+
+For most continuous features, the \texttt{mean} is used.
+For cyclic or categorical features—such as \textit{day of the week}— the \texttt{max} or \texttt{mode} is applied to avoid introducing
+artifacts at bin boundaries, where values from distinct categories (e.g., hours 23 and 0) might otherwise be averaged into a nonexistent intermediate state.
+
+The effect of different sampling resolutions and aggregation strategies is evaluated in Section~\ref{sec:results}.
+
+To simulate real-time prediction rather than retrospective analysis, a sliding-window approach is employed.
+This allows the model to make partial predictions based on the data available up to a certain point in the cycle.
+
+Each cycle is split into overlapping input windows, where each window includes data from the start of the cycle up to a specific day.
+The window length is fixed and configurable.
+As the cycle progresses, the window slides forward, allowing the model to incorporate increasing historical context over time.
+
+This setup enables temporally resolved predictions at different stages of the cycle and supports evaluation of how predictive accuracy changes with increasing context.
+
+\begin{figure}[htbp]
+    \centering
+    \includegraphics[width=0.9\textwidth]{methodology_padding_example}
+    \caption{
+        Padded input window during early cycle phases,
+        where historical data is still sparse.
+    }
+    \label{fig:methodology_padding_example}
+\end{figure}
+
+Fixed-length input windows would normally prevent early-cycle predictions when insufficient data is available.
+To address this, left-padding is applied using masked values.
+
+In this study, predictions are enabled once at least 4 days of data are available.
+A padding value of 0.0 is used for all features, and the padding length is adjusted accordingly.
+This choice ensures that the model learns to ignore tokens consisting entirely of padding.
+
+The feature \textit{hours since start}, which encodes the time elapsed since cycle onset, will be zero for all padded tokens—explicitly indicating
+that these entries contain no meaningful information.
+
+Figure~\ref{fig:methodology_padding_example} shows an example of such padding during early-cycle input preparation.
+
 \subsection{Model Training}\label{subsec:model_training}
 
 \subsubsection{Training Setup}\label{subsubsec:training_setup}