Alex Blank
688 lines
51 KiB
TeX
688 lines
51 KiB
TeX
\appendix
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\appendixpage % prints "Appendices"
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\section{Supplementary Results: Prediction of Ovulation-Over Indicator}\label{sec:appendix_ov_over_results}
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\subsection{Comparative Study of Model Architectures and Parameters}\label{subsec:appendix_ov_over_architecture_results}
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We evaluate prediction of a binary indicator denoting whether ovulation has already occurred.
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We examine the influence of input window length, input resolution, and model capacity.
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\paragraph{Impact of input window length.}
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For predicting whether ovulation has occurred (OV-over),
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optimal window lengths again vary by architecture and target phase (pre- vs post-ovulation).
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The \textbf{Transformer} yields the lowest overall (0.0533) and post-ovulation MSE (0.0520) at 40 days.
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The \textbf{LSTM} performs best before ovulation (MSE 0.0212 at 20 days), while its overall MSE improves with longer context (160 days).
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The \textbf{Convolutional LSTM} favors short windows, with the best overall MSE (0.0699), before-OV (0.0389),
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and after-OV (0.0833) all occurring at 20 days.
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The \textbf{Convolutional Transformer} performs best overall at 40 days and best before ovulation at 160 days (MSE 0.0286).
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See Table~\ref{tab:ovover_windows_compact_mse} for a summary.
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\begin{table}[t]
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\small
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\renewcommand{\arraystretch}{1.15}
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\setlength{\tabcolsep}{6pt}
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\begin{tabularx}{\linewidth}{l*{3}{>{\centering\arraybackslash}X}}
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\toprule
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\textbf{Architecture} & \multicolumn{3}{c}{\textbf{MSE}} \\
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\cmidrule(r){2-4}
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& \shortstack[c]{Overall best\\(days / MSE)}
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& \shortstack[c]{Before-OV best\\(days / MSE)}
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& \shortstack[c]{After-OV best\\(days / MSE)} \\
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\midrule
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LSTM
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& 160 / 0.0557
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& \textbf{20 / 0.0212}
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& 160 / 0.0580 \\
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Transformer
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& \textbf{40 / 0.0533}
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& 20 / 0.0255
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& \textbf{40 / 0.0520} \\
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Convolutional LSTM
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& 20 / 0.0699
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& 20 / 0.0389
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& 20 / 0.0833 \\
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Convolutional Transformer
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& 40 / 0.0709
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& 160 / 0.0286
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& 40 / 0.0820 \\
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\bottomrule
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\end{tabularx}
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\caption{OV-Over: best input window per architecture (MSE only) at a fixed input resolution of 12 values/day.
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Bold entries are bests within a column.}
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\label{tab:ovover_windows_compact_mse}
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\end{table}
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\paragraph{Impact of input resolution.}
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Fixing the input window to 20 days, we compare input resolutions for LSTM and Transformer models.
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For the \textbf{LSTM}, the lowest overall MSE (0.0633) occurs at 24/day,
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with best before-OV and after-OV MSEs at 12/day (0.0212) and 48/day (0.0550), respectively.
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The \textbf{Transformer} performs best overall at 72/day (0.0585),
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with lowest before-OV MSE at 12/day (0.0255) and after-OV MSE at 288/day (0.0578).
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Results are summarized in Table~\ref{tab:ovover_resolution_compact_mse};
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full resolution grids are in Appendix Table~\ref{tab:ov_over_results_by_resolution}.
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\begin{table}[t]
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\small
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\renewcommand{\arraystretch}{1.15}
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\setlength{\tabcolsep}{6pt}
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\begin{tabularx}{\linewidth}{l*{3}{>{\centering\arraybackslash}X}}
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\toprule
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\textbf{Architecture} & \multicolumn{3}{c}{\textbf{MSE}} \\
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\cmidrule(r){2-4}
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& \shortstack[c]{Overall best\\(values/day / MSE)}
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& \shortstack[c]{Before-OV best\\(values/day / MSE)}
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& \shortstack[c]{After-OV best\\(values/day / MSE)} \\
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\midrule
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LSTM
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& 24 / 0.0633
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& \textbf{12 / 0.0212}
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& \textbf{48 / 0.0550} \\
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Transformer
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& \textbf{72 / 0.0585}
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& 12 / 0.0255
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& 288 / 0.0578 \\
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\bottomrule
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\end{tabularx}
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\caption{OV-Over: best input resolution per architecture (MSE only) at a fixed input-window length of 20\,days.
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Bold entries are bests within a column. Convolutional models are excluded (they consume 288 values/day internally).}
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\label{tab:ovover_resolution_compact_mse}
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\end{table}
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\paragraph{Impact of model parameters.}
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Using fixed input settings (160 days at 12/day for LSTM/Transformer; 40 days for convolutional models), we evaluate model scaling.
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The \textbf{Transformer} achieves the lowest overall (0.0543) and after-ovulation MSE (0.0410) at a large configuration (512×8×8).
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Before-OV MSE is lowest at 256×4×4 (0.0293).
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For the \textbf{LSTM}, the best overall and after-OV performance is at 512×4,
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while the best before-OV MSE occurs at 128×2 (0.0274).
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The \textbf{Convolutional LSTM} performs best overall and after-OV at 256×4, with the best before-OV MSE at 128×2.
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Finally, the \textbf{Convolutional Transformer} achieves its lowest overall and after-OV MSE at 512×4×4, and best before-OV MSE at 512×8×8.
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Table~\ref{tab:ovover_params_compact_mse} summarizes these parameter-dependent results;
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full comparisons are included in Appendix Tables~\ref{tab:ov_over_results_by_model_parameters_lstm}–\ref{tab:ov_over_results_by_model_parameters_conv_transformer}.
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\begin{table}[t]
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\scriptsize
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\renewcommand{\arraystretch}{1.15}
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\setlength{\tabcolsep}{6pt}
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\begin{tabularx}{\linewidth}{l*{3}{>{\centering\arraybackslash}X}}
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\toprule
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\textbf{Architecture} & \multicolumn{3}{c}{\textbf{MSE}} \\
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\cmidrule(r){2-4}
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& \shortstack[c]{Overall best\\(params / MSE)}
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& \shortstack[c]{Before-OV best\\(params / MSE)}
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& \shortstack[c]{After-OV best\\(params / MSE)} \\
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\midrule
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LSTM
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& \(512\times4\) / 0.0616
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& \textbf{\(128\times2\) / 0.0274}
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& \(512\times4\) / 0.0613 \\
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Transformer
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& \textbf{\(512\times8\times8\) / 0.0543}
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& \(256\times4\times4\) / 0.0293
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& \textbf{\(512\times8\times8\) / 0.0410} \\
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Convolutional LSTM
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& \(256\times4\) / 0.0687
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& \(128\times2\) / 0.0357
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& \(256\times4\) / 0.0715 \\
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Convolutional Transformer
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& \(512\times4\times4\) / 0.0703
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& \(512\times8\times8\) / 0.0339
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& \(512\times4\times4\) / 0.0814 \\
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\bottomrule
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\end{tabularx}
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\caption{OV-Over: best parameter settings per architecture (MSE only).
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Parameters are formatted as \(\text{hidden}\times\text{layers}\) (LSTM/Conv.\ LSTM) and \(\text{embedding}\times\text{encoder layers}\times\text{heads}\) (Transformer/Conv.\ Transformer).
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Bold values indicate bests within a column. Fixed input settings: 160\,days with 12 values/day for LSTM/Transformer; 40\,days for convolutional models.}
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\label{tab:ovover_params_compact_mse}
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\end{table}
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\subsection{Regular vs. Irregular Cycles}\label{subsec:appendix_ov_over_regular_vs_irregular}
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\begin{table}[htbp]
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\centering
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\small
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\begin{tabularx}{\linewidth}{l*{3}{X}}
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\toprule
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\multirow{2}{*}{Model} & \multicolumn{3}{c}{MSE} \\
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\cmidrule(r){2-4}
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& OV-Over Overall & OV-Over Before OV & OV-Over After OV \\
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\midrule
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\multicolumn{4}{c}{\textbf{Regular Cycle Group}} \\
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\midrule
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LSTM & \textbf{0.028519} & 0.031695 & \textbf{0.026116} \\
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Transformer & 0.032929 & \textbf{0.024774} & 0.033411 \\
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Convolutional LSTM & 0.034224 & 0.028962 & 0.033929 \\
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Convolutional Transformer & 0.033333 & 0.039045 & 0.029329 \\
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Last-Cycle Baseline & 0.087916 & 0.093465 & 0.074191 \\
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Population Mean Baseline & 0.285887 & 0.008876 & 0.418879 \\
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User Mean Baseline & 0.069245 & 0.069152 & 0.055724 \\
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\midrule
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\multicolumn{4}{c}{\textbf{Irregular Cycle Group}} \\
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\midrule
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LSTM & \textbf{0.051747} & \textbf{0.016777} & 0.070419 \\
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Transformer & 0.055990 & 0.026159 & 0.067520 \\
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Convolutional LSTM & 0.054523 & 0.030092 & \textbf{0.066643} \\
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Convolutional Transformer & 0.056075 & 0.029638 & 0.067965 \\
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Last-Cycle Baseline & 0.222906 & 0.134945 & 0.258047 \\
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Population Mean Baseline & 0.171368 & 0.130366 & 0.140363 \\
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User Mean Baseline & 0.180702 & 0.088884 & 0.217144 \\
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\bottomrule
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\end{tabularx}
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\caption{MSEs for ovulation-over target across different model architectures for regular and irregular cycle groups. Bold values denote the best scores per column.}
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\label{tab:ov_over_mse_regular_irregular}
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\end{table}
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\noindent
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Table~\ref{tab:ov_over_mse_regular_irregular} shows the MSE metric results for all models on the regular and irregular cycle groups for the OV-over target.
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For this target, performance patterns differ more distinctly across phases.
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In the regular group, the LSTM performs best overall and after ovulation, while the Transformer performs best before ovulation.
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Convolutional models perform slightly worse than their recurrent and transformer-based counterparts across all phases.
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The population mean baseline shows a spurious low MSE before ovulation, but this is not consistent across phases, suggesting it is not reliable.
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In general, baselines are notably weaker than learned models across all splits.
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For irregular cycles, all model performances deteriorate relative to regular cycles.
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The LSTM remains the most robust, achieving the best MSEs both overall and before ovulation.
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Convolutional LSTM performs best after ovulation.
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Transformer-based models perform less consistently in this group, suggesting decreased robustness to cycle irregularity.
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As before, all learned models outperform the baselines by a wide margin.
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The full table with MSE and MAE for all models can be found in the appendix, Table~\ref{tab:regular_vs_irregular_ov_over_results}.
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\section{Extra Figures}\label{app:figs}
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\begin{landscape}
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\begin{table}
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\small
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\begin{tabularx}{\linewidth}{l*{6}{X}}
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\toprule
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\multirow{2}{*}{Input-Length in Days} & \multicolumn{3}{c}{MAE} & \multicolumn{3}{c}{MSE} \\
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\cmidrule(r){2-4} \cmidrule(r){5-7}
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& Fertility Overall & Fertile Days & Non-Fertile Days & Fertility Overall & Fertile Days & Non-Fertile Days \\
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\midrule
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\multicolumn{7}{c}{\textbf{LSTM}} \\ \midrule
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10 & 0.0399 & 0.0857 & 0.0212 & 0.0045 & 0.0105 & 0.0021 \\
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20 & 0.0421 & 0.0994 & 0.0180 & 0.0052 & 0.0144 & \underline{\textbf{0.0013}} \\
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40 & 0.0406 & 0.0938 & 0.0184 & 0.0049 & 0.0128 & 0.0016 \\
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80 & 0.0420 & 0.1006 & \underline{\textbf{0.0176}} & 0.0053 & 0.0147 & 0.0014 \\
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160 & \underline{0.0378} & \underline{\textbf{0.0834}} & 0.0192 & \underline{0.0043} & \underline{\textbf{0.0102}} & 0.0019 \\
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\midrule
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\multicolumn{7}{c}{\textbf{Transformer}} \\ \midrule
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10 & 0.0443 & 0.0868 & 0.0273 & 0.0046 & 0.0110 & 0.0021 \\
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20 & 0.0472 & 0.0949 & 0.0274 & 0.0051 & 0.0133 & 0.0018 \\
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40 & 0.0413 & 0.0861 & 0.0233 & \underline{0.0044} & 0.0111 & 0.0017 \\
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80 & 0.0437 & \underline{0.0859} & 0.0269 & 0.0045 & \underline{0.0108} & 0.0021 \\
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160 & \underline{0.0411} & 0.0882 & \underline{0.0218} & 0.0045 & 0.0116 & \underline{0.0016} \\
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\midrule
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\multicolumn{7}{c}{\textbf{Convolutional-LSTM}} \\ \midrule
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10 & 0.0435 & 0.0954 & 0.0224 & 0.0050 & 0.0133 & 0.0017 \\
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20 & 0.0414 & 0.0996 & \underline{0.0179} & 0.0050 & 0.0145 & \underline{\textbf{0.0013}} \\
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40 & \underline{0.0394} & \underline{0.0911} & 0.0184 & \underline{0.0045} & \underline{0.0122} & 0.0014 \\
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80 & 0.0481 & 0.1005 & 0.0278 & 0.0054 & 0.0146 & 0.0019 \\
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160 & 0.0505 & 0.1067 & 0.0290 & 0.0060 & 0.0166 & 0.0020 \\
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\midrule
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\multicolumn{7}{c}{\textbf{Convolutional-Transformer}} \\ \midrule
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10 & 0.0459 & 0.0962 & 0.0256 & 0.0049 & 0.0136 & \underline{0.0015} \\
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20 & 0.0423 & \underline{0.0848} & 0.0257 & 0.0045 & \underline{\textbf{0.0102}} & 0.0022 \\
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40 & \underline{\textbf{0.0376}} & 0.0854 & \underline{0.0186} & \underline{\textbf{0.0041}} & 0.0108 & \underline{0.0015} \\
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80 & 0.0414 & 0.0932 & 0.0210 & 0.0048 & 0.0128 & 0.0018 \\
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160 & 0.0523 & 0.1018 & 0.0337 & 0.0059 & 0.0148 & 0.0026 \\
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\bottomrule
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\end{tabularx}
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\caption{Evaluation Metrics for the Fertility-Probability Target across Different Model Architectures and Input Lengths at a fixed Input Resolution of 12 Values per Day.
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\underline{Underlined} values represent the best value for each metric within a model.
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\textbf{\underline{Bold + Underlined}} values represent the global best values across all models for a given metric.}
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\label{tab:fertility_results_by_window_length}
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\end{table}
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\end{landscape}
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\begin{landscape}
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\begin{table}
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\small
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\begin{tabularx}{\linewidth}{l*{6}{X}}
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\toprule
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\multirow{2}{*}{Values Per Day} & \multicolumn{3}{c}{MAE} & \multicolumn{3}{c}{MSE} \\
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\cmidrule(r){2-4} \cmidrule(r){5-7}
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& Fertility Overall & Fertile Days & Non-Fertile Days & Fertility Overall & Fertile Days & Non-Fertile Days \\
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\midrule
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\multicolumn{7}{c}{\textbf{LSTM}} \\
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\midrule
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1 & 0.0471 & 0.0987 & 0.0220 & 0.0062 & 0.0145 & 0.0022 \\
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2 & 0.0457 & 0.0962 & 0.0231 & 0.0052 & 0.0133 & 0.0016 \\
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4 & 0.0421 & 0.0898 & 0.0223 & \underline{\textbf{0.0046}}& 0.0116 & 0.0018 \\
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12 & 0.0421 & 0.0994 & \underline{\textbf{0.0180}}& 0.0052 & 0.0144 & \underline{\textbf{0.0013}}\\
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24 & 0.0419 & 0.0949 & 0.0201 & 0.0050 & 0.0132 & 0.0017 \\
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48 & \underline{\textbf{0.0402}}& 0.0929 & 0.0182 & 0.0049 & 0.0126 & 0.0018 \\
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72 & 0.0433 & 0.0972 & 0.0216 & 0.0052 & 0.0137 & 0.0018 \\
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288 & 0.0410 & \underline{\textbf{0.0872}}& 0.0223 & 0.0049 & \underline{\textbf{0.0110}}& 0.0025 \\
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\midrule
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\multicolumn{7}{c}{\textbf{Transformer}} \\
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\midrule
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1 & 0.0541 & 0.1015 & 0.0313 & 0.0063 & 0.0146 & 0.0023 \\
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2 & 0.0468 & 0.0946 & 0.0259 & 0.0054 & 0.0128 & 0.0021 \\
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4 & 0.0456 & \underline{0.0891}& 0.0277 & 0.0050 & \underline{0.0115}& 0.0023 \\
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12 & 0.0472 & 0.0949 & 0.0274 & 0.0051 & 0.0133 & 0.0018 \\
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24 & 0.0502 & 0.0960 & 0.0323 & 0.0052 & 0.0133 & 0.0021 \\
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48 & 0.0447 & 0.0912 & \underline{0.0254}& \underline{0.0048}& 0.0122 & 0.0017 \\
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72 & 0.0468 & 0.0975 & 0.0257 & 0.0051 & 0.0140 & \underline{0.0014} \\
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288 & \underline{0.0449}& 0.0937 & 0.0257 & \underline{0.0048}& 0.0128 & 0.0017 \\
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\bottomrule
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\end{tabularx}
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\caption{Evaluation Metrics for the Fertility-Probability Target across Different Model Architectures and Input Resolutions at a fixed Input-Window-Length of 20 Days.
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\underline{Underlined} values represent the best value for each metric within a model.
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\textbf{\underline{Bold + Underlined}} values represent the global best values across all models for a given metric.}
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\label{tab:fertility_results_by_window_resolution}
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\end{table}
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\end{landscape}
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\begin{landscape}
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\begin{table}
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\scriptsize
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\begin{tabularx}{\linewidth}{l*{8}{X}}
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\midrule
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\multicolumn{8}{c}{\textbf{LSTM}} \\
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\toprule
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\multirow{2}{*}{Hidden Layer Size} &
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\multirow{2}{*}{\# LSTM Layers} &
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\multicolumn{3}{c}{MAE} &
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\multicolumn{3}{c}{MSE} \\
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\cmidrule(lr){3-5} \cmidrule(lr){6-8}
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& & Fert Overall & Fert Days & Non-Fert Days & Fert Overall & Fert Days & Non-Fert Days \\
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\midrule
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16 & 1 & 0.0485 & 0.1050 & 0.0252 & 0.0059 & 0.0161 & 0.0016 \\
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32 & 1 & 0.0477 & 0.1029 & 0.0248 & 0.0057 & 0.0155 & 0.0016 \\
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32 & 2 & 0.0458 & 0.0922 & 0.0269 & 0.0052 & \textbf{0.0122} & 0.0024 \\
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64 & 2 & 0.0447 & 0.0925 & 0.0250 & 0.0051 & 0.0123 & 0.0021 \\
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128 & 2 & 0.0424 & 0.0951 & 0.0201 & 0.0051 & 0.0131 & 0.0018 \\
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128 & 4 & 0.0427 & 0.0989 & 0.0191 & 0.0053 & 0.0145 & 0.0014 \\
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256 & 4 & 0.0433 & 0.1050 & \textbf{0.0175} & 0.0055 & 0.0161 & \textbf{0.0011} \\
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512 & 4 & \textbf{0.0399} & \textbf{0.0911} & 0.0189 & \textbf{0.0047} & \textbf{0.0122} & 0.0016 \\
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\bottomrule
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\end{tabularx}
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\caption{Evaluation Metrics for the fertility-probability target across Different Model Parameters for the LSTM model with
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a fixed input window length of 160 days and an input resolution of 12 values per day.
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\textbf{Bold} values represent the best value for each metric within a model.}
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\label{tab:fertility_results_by_model_parameters_lstm}
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\end{table}
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\begin{table}
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\scriptsize
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\begin{tabularx}{\linewidth}{l*{8}{X}}
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\midrule
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\multicolumn{9}{c}{\textbf{Transformer}} \\
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\toprule
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\multirow{2}{*}{Size of Embedding} &
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\multirow{2}{*}{\# Encoder Layers} &
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\multirow{2}{*}{\# Attention Heads} &
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\multicolumn{3}{c}{MAE} &
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\multicolumn{3}{c}{MSE} \\
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\cmidrule(lr){4-6} \cmidrule(lr){7-9}
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& & & Fert Overall & Fert Days & Non-Fert Days & Fert Overall & Fert Days & Non-Fert Days \\
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\midrule
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16 & 1 & 1 & 0.0708 & 0.1262 & 0.0475 & 0.0084 & 0.0227 & 0.0024 \\
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32 & 1 & 1 & 0.0599 & 0.1163 & 0.0378 & 0.0070 & 0.0196 & 0.0019 \\
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64 & 1 & 1 & 0.0601 & 0.1178 & 0.0373 & 0.0071 & 0.0200 & 0.0020 \\
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64 & 2 & 2 & 0.0477 & 0.0908 & 0.0301 & 0.0049 & 0.0121 & 0.0020 \\
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128 & 2 & 2 & 0.0449 & 0.0904 & 0.0263 & 0.0047 & 0.0119 & 0.0017 \\
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128 & 4 & 4 & 0.0461 & 0.0979 & 0.0247 & 0.0050 & 0.0143 & 0.0013 \\
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256 & 4 & 4 & 0.0475 & 0.0919 & 0.0293 & 0.0048 & 0.0125 & 0.0017 \\
|
||
512 & 4 & 4 & 0.0403 & \textbf{0.0831} & 0.0229 & \textbf{0.0043} & \textbf{0.0103} & 0.0018 \\
|
||
512 & 8 & 8 & \textbf{0.0395} & 0.0967 & \textbf{0.0159} & 0.0048 & 0.0139 & \textbf{0.0011} \\
|
||
\bottomrule
|
||
\end{tabularx}
|
||
\caption{Evaluation Metrics for the fertility probability target across Different Model Parameters for the Transformer model with
|
||
a fixed input window length of 160 days and an input resolution of 12 values per day.
|
||
\textbf{Bold} values represent the best value for each metric within a model.}
|
||
\label{tab:fertility_results_by_model_parameters_transformer}
|
||
\end{table}
|
||
|
||
\begin{table}
|
||
\scriptsize
|
||
\begin{tabularx}{\linewidth}{l*{8}{X}}
|
||
\midrule
|
||
\multicolumn{8}{c}{\textbf{Convolutional-LSTM}} \\
|
||
\toprule
|
||
\multirow{2}{*}{Hidden Layer Size} &
|
||
\multirow{2}{*}{\# LSTM Layers} &
|
||
\multicolumn{3}{c}{MAE} &
|
||
\multicolumn{3}{c}{MSE} \\
|
||
\cmidrule(lr){3-5} \cmidrule(lr){6-8}
|
||
& & Fert Overall & Fert Days & Non-Fert Days & Fert Overall & Fert Days & Non-Fert Days \\
|
||
\midrule
|
||
16 & 1 & 0.0487 & 0.1029 & 0.0264 & 0.0056 & 0.0154 & \textbf{0.0016} \\
|
||
32 & 1 & 0.0461 & 0.0956 & 0.0266 & 0.0050 & 0.0133 & 0.0017 \\
|
||
32 & 2 & 0.0448 & 0.0975 & 0.0232 & 0.0051 & 0.0139 & \textbf{0.0016} \\
|
||
64 & 2 & 0.0432 & 0.0921 & 0.0241 & 0.0048 & 0.0123 & 0.0019 \\
|
||
128 & 2 & 0.0400 & 0.0895 & 0.0204 & 0.0045 & 0.0119 & \textbf{0.0016} \\
|
||
128 & 4 & 0.0398 & 0.0871 & 0.0204 & 0.0044 & 0.0113 & \textbf{0.0016} \\
|
||
256 & 4 & 0.0389 & \textbf{0.0826} & 0.0209 & \textbf{0.0042} & \textbf{0.0100} & 0.0018 \\
|
||
512 & 4 & \textbf{0.0380} & 0.0869 & \textbf{0.0184} & 0.0044 & 0.0112 & 0.0017 \\
|
||
\bottomrule
|
||
\end{tabularx}
|
||
\caption{Evaluation Metrics for the fertility probability target across Different Model Parameters for the convolutional LSTM model with
|
||
a fixed input window length of 40 days.
|
||
\textbf{Bold} values represent the best value for each metric within a model.}
|
||
\label{tab:fertility_results_by_model_parameters_conv_lstm}
|
||
\end{table}
|
||
\begin{table}
|
||
\scriptsize
|
||
\begin{tabularx}{\linewidth}{l*{8}{X}}
|
||
\midrule
|
||
\multicolumn{8}{c}{\textbf{Convolutional-Transformer}} \\
|
||
\toprule
|
||
\multirow{2}{*}{Size of Embedding} &
|
||
\multirow{2}{*}{\# Encoder Layers} &
|
||
\multirow{2}{*}{\# Attention Heads} &
|
||
\multicolumn{3}{c}{MAE} &
|
||
\multicolumn{3}{c}{MSE} \\
|
||
\cmidrule(lr){4-6} \cmidrule(lr){7-9}
|
||
& & & Fert Overall & Fert Days & Non-Fert Days & Fert Overall & Fert Days & Non-Fert Days \\
|
||
\midrule
|
||
16 & 1 & 1 & 0.0574 & 0.1005 & 0.0427 & 0.0061 & 0.0146 & 0.0032 \\
|
||
32 & 1 & 1 & 0.0462 & 0.0963 & 0.0266 & 0.0051 & 0.0135 & 0.0018 \\
|
||
64 & 1 & 1 & 0.0546 & 0.1006 & 0.0379 & 0.0059 & 0.0145 & 0.0027 \\
|
||
64 & 2 & 2 & 0.0427 & 0.0903 & 0.0237 & 0.0046 & 0.0118 & 0.0018 \\
|
||
128 & 2 & 2 & 0.0406 & 0.0868 & 0.0225 & 0.0044 & 0.0113 & 0.0017 \\
|
||
128 & 4 & 4 & 0.0411 & 0.0886 & 0.0230 & 0.0044 & 0.0116 & 0.0017 \\
|
||
256 & 4 & 4 & 0.0420 & 0.0866 & 0.0240 & \textbf{0.0043} & 0.0113 & 0.0016 \\
|
||
512 & 4 & 4 & \textbf{0.0377} & 0.0878 & \textbf{0.0177} & \textbf{0.0043} & 0.0117 & \textbf{0.0013} \\
|
||
512 & 8 & 8 & 0.0399 & \textbf{0.0842} & 0.0224 & 0.0044 & \textbf{0.0107} & 0.0019 \\
|
||
\bottomrule
|
||
\end{tabularx}
|
||
\caption{Evaluation Metrics for the fertility probability target across Different Model Parameters for the convolutional Transformer model with
|
||
a fixed input window length of 40 days.
|
||
\textbf{Bold} values represent the best value for each metric within a model.}
|
||
\label{tab:fertility_results_by_model_parameters_conv_transformer}
|
||
\end{table}
|
||
\end{landscape}
|
||
|
||
\begin{landscape}
|
||
\begin{table}
|
||
\small
|
||
\begin{tabularx}{\linewidth}{l*{6}{X}}
|
||
\toprule
|
||
\multirow{2}{*}{Input-Length in Days} & \multicolumn{3}{c}{MAE} & \multicolumn{3}{c}{MSE} \\
|
||
\cmidrule(r){2-4} \cmidrule(r){5-7}
|
||
& OV-Over Overall & OV-Over Before OV & OV-Over After OV & OV-Over Overall & OV-Over Before OV & OV-Over After OV \\
|
||
\midrule
|
||
\multicolumn{7}{c}{\textbf{LSTM}} \\
|
||
\midrule
|
||
10 & 0.1218 & 0.1044 & 0.1223 & 0.0612 & 0.0289 & 0.0711 \\
|
||
20 & 0.1153 & \underline{\textbf{0.0745}} & 0.1312 & 0.0641 & \underline{\textbf{0.0212}} & 0.0822 \\
|
||
40 & 0.1066 & 0.0820 & 0.1128 & 0.0616 & 0.0281 & 0.0740 \\
|
||
80 & 0.1173 & 0.0842 & 0.1291 & 0.0647 & 0.0263 & 0.0801 \\
|
||
160 & \underline{0.1039} & 0.1139 & \underline{0.0973} & \underline{0.0557} & 0.0462 & \underline{0.0580} \\
|
||
\midrule
|
||
\multicolumn{7}{c}{\textbf{Transformer}} \\
|
||
\midrule
|
||
10 & 0.1137 & 0.1006 & 0.1186 & 0.0618 & 0.0336 & 0.0740 \\
|
||
20 & 0.1204 & 0.0771 & 0.1366 & 0.0690 & \underline{0.0255} & 0.0864 \\
|
||
40 & \underline{\textbf{0.1017}} & 0.1409 & \underline{\textbf{0.0883}} & \underline{\textbf{0.0533}} & 0.0621 & \underline{\textbf{0.0520}} \\
|
||
80 & 0.1138 & \underline{0.0897} & 0.1234 & 0.0654 & 0.0356 & 0.0788 \\
|
||
160 & 0.1076 & 0.0959 & 0.1141 & 0.0606 & 0.0379 & 0.0714 \\
|
||
\midrule
|
||
\multicolumn{7}{c}{\textbf{Convolutional-LSTM}} \\
|
||
\midrule
|
||
10 & 0.1847 & 0.1878 & 0.1840 & 0.0819 & 0.0581 & 0.0949 \\
|
||
20 & 0.1493 & 0.1274 & 0.1562 & \underline{0.0699} & \underline{0.0389} & \underline{0.0833} \\
|
||
40 & \underline{0.1455} & \underline{0.1089} & \underline{0.1561} & 0.0722 & 0.0358 & 0.0852 \\
|
||
80 & 0.2327 & 0.1796 & 0.2507 & 0.1168 & 0.0686 & 0.1327 \\
|
||
160 & 0.2518 & 0.2040 & 0.2757 & 0.1293 & 0.0878 & 0.1503 \\
|
||
\midrule
|
||
\multicolumn{7}{c}{\textbf{Convolutional-Transformer}} \\
|
||
\midrule
|
||
10 & 0.1472 & 0.1053 & 0.1651 & 0.0768 & 0.0317 & 0.0966 \\
|
||
20 & 0.1530 & 0.1307 & 0.1637 & 0.0745 & 0.0443 & 0.0886 \\
|
||
40 & \underline{0.1448} & 0.1228 & \underline{0.1514} & \underline{0.0709} & 0.0435 & \underline{0.0820} \\
|
||
80 & 0.1685 & 0.1164 & 0.1889 & 0.0865 & 0.0345 & 0.1089 \\
|
||
160 & 0.2440 & \underline{0.1051} & 0.3117 & 0.1403 & \underline{0.0286} & 0.1946 \\
|
||
\bottomrule
|
||
|
||
\end{tabularx}
|
||
\caption{Evaluation Metrics for the Ovulation-Over Target across Different Model Architectures and Input Lengths at a fixed Input Resolution of 12 Values per Day.
|
||
\underline{Underlined} values represent the best value for each metric within a model.
|
||
\textbf{\underline{Bold + Underlined}} values represent the global best values across all models for a given metric.}
|
||
\label{tab:ov_over_results_by_window_length}
|
||
\end{table}
|
||
\end{landscape}
|
||
|
||
\begin{landscape}
|
||
\begin{table}
|
||
\small
|
||
\begin{tabularx}{\linewidth}{l*{6}{X}}
|
||
\toprule
|
||
\multirow{2}{*}{Values Per Day} & \multicolumn{3}{c}{MAE} & \multicolumn{3}{c}{MSE} \\
|
||
\cmidrule(r){2-4} \cmidrule(r){5-7}
|
||
& OV-Over Overall & OV-Over Before OV & OV-Over After OV & OV-Over Overall & OV-Over Before OV & OV-Over After OV \\
|
||
\midrule
|
||
\multicolumn{7}{c}{\textbf{LSTM}} \\
|
||
\midrule
|
||
1 & 0.1691 & 0.1280 & 0.1819 & 0.0914 & 0.0411 & 0.1099 \\
|
||
2 & 0.1431 & 0.1239 & 0.1471 & 0.0747 & 0.0442 & 0.0849 \\
|
||
4 & 0.1344 & 0.1130 & 0.1417 & 0.0680 & 0.0371 & 0.0807 \\
|
||
12 & \underline{0.1153} & \underline{\textbf{0.0745}} & 0.1312 & 0.0641 & \underline{\textbf{0.0212}} & 0.0822 \\
|
||
24 & 0.1192 & 0.0975 & 0.1240 & \underline{0.0633} & 0.0288 & 0.0755 \\
|
||
48 & 0.1463 & 0.2493 & \underline{0.0973} & 0.0768 & 0.1203 & \underline{\textbf{0.0550}} \\
|
||
72 & 0.1587 & 0.1113 & 0.1844 & 0.0857 & 0.0341 & 0.1126 \\
|
||
288 & 0.1353 & 0.1801 & 0.1169 & 0.0726 & 0.0799 & 0.0713 \\
|
||
\midrule
|
||
\multicolumn{7}{c}{\textbf{Transformer}} \\
|
||
\midrule
|
||
1 & 0.1687 & 0.1582 & 0.1709 & 0.0883 & 0.0578 & 0.0996 \\
|
||
2 & 0.1469 & 0.0895 & 0.1676 & 0.0823 & 0.0260 & 0.1044 \\
|
||
4 & 0.1282 & 0.0999 & 0.1393 & 0.0704 & 0.0329 & 0.0862 \\
|
||
12 & 0.1204 & \underline{0.0771}& 0.1366 & 0.0690 & \underline{0.0255}& 0.0864 \\
|
||
24 & 0.1952 & 0.1649 & 0.2102 & 0.0904 & 0.0638 & 0.1040 \\
|
||
48 & 0.1137 & 0.1159 & 0.1073 & 0.0610 & 0.0480 & 0.0629 \\
|
||
72 & \underline{\textbf{0.1041}}& 0.1180 & \underline{\textbf{0.0947}}& \underline{\textbf{0.0585}}& 0.0538 & 0.0581 \\
|
||
288 & 0.1319 & 0.1879 & 0.1097 & 0.0617 & 0.0769 & \underline{0.0578}\\
|
||
\bottomrule
|
||
\end{tabularx}
|
||
\caption{Evaluation Metrics for the Ovulation-Over Target across Different Model Architectures and Input Resolutions at a fixed Input-Window-Length of 20 Days.
|
||
\underline{Underlined} values represent the best value for each metric within a model.
|
||
\textbf{\underline{Bold + Underlined}} values represent the global best values across all models for a given metric.}
|
||
\label{tab:ov_over_results_by_resolution}
|
||
\end{table}
|
||
\end{landscape}
|
||
|
||
\begin{landscape}
|
||
\begin{table}
|
||
\scriptsize
|
||
\begin{tabularx}{\linewidth}{l*{8}{X}}
|
||
\midrule
|
||
\multicolumn{8}{c}{\textbf{LSTM}} \\
|
||
\toprule
|
||
\multirow{2}{*}{Hidden Layer Size} &
|
||
\multirow{2}{*}{\# LSTM Layers} &
|
||
\multicolumn{3}{c}{MAE} &
|
||
\multicolumn{3}{c}{MSE} \\
|
||
\cmidrule(lr){3-5} \cmidrule(lr){6-8}
|
||
& & Fert Overall & Fert Days & Non-Fert Days & Fert Overall & Fert Days & Non-Fert Days \\
|
||
\midrule
|
||
16 & 1 & 0.2253 & 0.1353 & 0.2735 & 0.1196 & 0.0397 & 0.1621 \\
|
||
32 & 1 & 0.1746 & 0.1255 & 0.2005 & 0.0860 & 0.0404 & 0.1109 \\
|
||
32 & 2 & 0.1349 & 0.1240 & 0.1399 & 0.0674 & 0.0396 & 0.0786 \\
|
||
64 & 2 & 0.1270 & 0.0943 & 0.1365 & 0.0645 & 0.0292 & 0.0772 \\
|
||
128 & 2 & 0.1151 & \textbf{0.0861} & 0.1243 & 0.0626 & \textbf{0.0274} & 0.0755 \\
|
||
128 & 4 & 0.1326 & 0.1064 & 0.1407 & 0.0658 & 0.0291 & 0.0811 \\
|
||
256 & 4 & 0.1353 & 0.1111 & 0.1407 & 0.0662 & 0.0360 & 0.0765 \\
|
||
512 & 4 & \textbf{0.1120} & 0.1358 & \textbf{0.0983} & \textbf{0.0616} & 0.0603 & \textbf{0.0613} \\
|
||
\bottomrule
|
||
\end{tabularx}
|
||
\caption{Evaluation Metrics for the Ovulation-Over Target across Different Model Parameters for the LSTM model with
|
||
a fixed input window length of 160 days and an input resolution of 12 values per day.
|
||
\textbf{Bold} values represent the best value for each metric within a model.}
|
||
\label{tab:ov_over_results_by_model_parameters_lstm}
|
||
\end{table}
|
||
\begin{table}
|
||
\scriptsize
|
||
\begin{tabularx}{\linewidth}{l*{8}{X}}
|
||
\midrule
|
||
\multicolumn{9}{c}{\textbf{Transformer}} \\
|
||
\toprule
|
||
\multirow{2}{*}{Size of Embedding} &
|
||
\multirow{2}{*}{\# Encoder Layers} &
|
||
\multirow{2}{*}{\# Attention Heads} &
|
||
\multicolumn{3}{c}{MAE} &
|
||
\multicolumn{3}{c}{MSE} \\
|
||
\cmidrule(lr){4-6} \cmidrule(lr){7-9}
|
||
& & & Fert Overall & Fert Days & Non-Fert Days & Fert Overall & Fert Days & Non-Fert Days \\
|
||
\midrule
|
||
16 & 1 & 1 & 0.4792 & 0.5280 & 0.4675 & 0.2336 & 0.2820 & 0.2220 \\
|
||
32 & 1 & 1 & 0.4406 & 0.4572 & 0.4417 & 0.2046 & 0.2179 & 0.2068 \\
|
||
64 & 1 & 1 & 0.3614 & 0.3411 & 0.3796 & 0.1696 & 0.1604 & 0.1800 \\
|
||
64 & 2 & 2 & 0.1532 & \textbf{0.0810} & 0.1913 & 0.0904 & 0.0302 & 0.1214 \\
|
||
128 & 2 & 2 & 0.1476 & 0.0979 & 0.1739 & 0.0816 & 0.0332 & 0.1064 \\
|
||
128 & 4 & 4 & \textbf{0.1114} & 0.0887 & 0.1236 & 0.0668 & 0.0368 & 0.0821 \\
|
||
256 & 4 & 4 & 0.1310 & 0.0920 & 0.1433 & 0.0722 & \textbf{0.0293} & 0.0876 \\
|
||
512 & 4 & 4 & 0.1222 & 0.1047 & 0.1261 & 0.0610 & 0.0321 & 0.0708 \\
|
||
512 & 8 & 8 & 0.1126 & 0.1965 & \textbf{0.0753} & \textbf{0.0543} & 0.0868 & \textbf{0.0410} \\
|
||
\bottomrule
|
||
\end{tabularx}
|
||
\caption{Evaluation Metrics for the Ovulation-Over Target across Different Model Parameters for the Transformer model with
|
||
a fixed input window length of 160 days and an input resolution of 12 values per day.
|
||
\textbf{Bold} values represent the best value for each metric within a model.}
|
||
\label{tab:ov_over_results_by_model_parameters_transformer}
|
||
\end{table}
|
||
|
||
\begin{table}
|
||
\scriptsize
|
||
\begin{tabularx}{\linewidth}{l*{8}{X}}
|
||
\midrule
|
||
\multicolumn{8}{c}{\textbf{Convolutional-LSTM}} \\
|
||
\toprule
|
||
\multirow{2}{*}{Hidden Layer Size} &
|
||
\multirow{2}{*}{\# LSTM Layers} &
|
||
\multicolumn{3}{c}{MAE} &
|
||
\multicolumn{3}{c}{MSE} \\
|
||
\cmidrule(lr){3-5} \cmidrule(lr){6-8}
|
||
& & Fert Overall & Fert Days & Non-Fert Days & Fert Overall & Fert Days & Non-Fert Days \\
|
||
\midrule
|
||
16 & 1 & 0.2920 & 0.2198 & 0.3344 & 0.1264 & 0.0602 & 0.1653 \\
|
||
32 & 1 & 0.2141 & 0.1598 & 0.2377 & 0.0912 & 0.0398 & 0.1149 \\
|
||
32 & 2 & 0.1990 & 0.1556 & 0.2135 & 0.0852 & 0.0419 & 0.1025 \\
|
||
64 & 2 & 0.1743 & 0.1345 & 0.1845 & 0.0789 & 0.0400 & 0.0923 \\
|
||
128 & 2 & 0.1524 & 0.1138 & 0.1633 & 0.0717 & \textbf{0.0357} & 0.0845 \\
|
||
128 & 4 & 0.1579 & 0.1185 & 0.1670 & 0.0777 & 0.0401 & 0.0894 \\
|
||
256 & 4 & \textbf{0.1424} & 0.1425 & \textbf{0.1369} & \textbf{0.0687} & 0.0546 & \textbf{0.0715} \\
|
||
512 & 4 & 0.1436 & \textbf{0.1166} & 0.1523 & 0.0699 & 0.0382 & 0.0820 \\
|
||
\bottomrule
|
||
\end{tabularx}
|
||
\caption{Evaluation Metrics for the Ovulation-Over Target across Different Model Parameters for the convolutional LSTM model with
|
||
a fixed input window length of 40 days.
|
||
\textbf{Bold} values represent the best value for each metric within a model.}
|
||
\label{tab:ov_over_results_by_model_parameters_conv_lstm}
|
||
\end{table}
|
||
\begin{table}
|
||
\scriptsize
|
||
\begin{tabularx}{\linewidth}{l*{8}{X}}
|
||
\midrule
|
||
\multicolumn{8}{c}{\textbf{Convolutional-Transformer}} \\
|
||
\toprule
|
||
\multirow{2}{*}{Size of Embedding} &
|
||
\multirow{2}{*}{\# Encoder Layers} &
|
||
\multirow{2}{*}{\# Attention Heads} &
|
||
\multicolumn{3}{c}{MAE} &
|
||
\multicolumn{3}{c}{MSE} \\
|
||
\cmidrule(lr){4-6} \cmidrule(lr){7-9}
|
||
& & & Fert Overall & Fert Days & Non-Fert Days & Fert Overall & Fert Days & Non-Fert Days \\
|
||
\midrule
|
||
16 & 1 & 1 & 0.3759 & 0.3602 & 0.3855 & 0.1597 & 0.1498 & 0.1659 \\
|
||
32 & 1 & 1 & 0.1757 & 0.1119 & 0.2017 & 0.0916 & 0.0314 & 0.1167 \\
|
||
64 & 1 & 1 & 0.2771 & 0.2395 & 0.2978 & 0.1303 & 0.1133 & 0.1402 \\
|
||
64 & 2 & 2 & 0.1610 & 0.1149 & 0.1777 & 0.0816 & \textbf{0.0310} & 0.1021 \\
|
||
128 & 2 & 2 & 0.1501 & 0.0997 & 0.1725 & 0.0783 & 0.0316 & 0.0991 \\
|
||
128 & 4 & 4 & 0.1668 & 0.1472 & 0.1758 & 0.0801 & 0.0488 & 0.0953 \\
|
||
256 & 4 & 4 & 0.1529 & 0.1317 & 0.1597 & 0.0742 & 0.0435 & 0.0861 \\
|
||
512 & 4 & 4 & \textbf{0.1495} & 0.1298 & \textbf{0.1551} & \textbf{0.0703} & 0.0409 & \textbf{0.0814} \\
|
||
512 & 8 & 8 & 0.1576 & \textbf{0.1025} & 0.1848 & 0.0825 & 0.0339 & 0.1066 \\
|
||
\bottomrule
|
||
\end{tabularx}
|
||
\caption{Evaluation Metrics for the Ovulation-Over Target across Different Model Parameters for the convolutional Transformer model with
|
||
a fixed input window length of 40 days.
|
||
\textbf{Bold} values represent the best value for each metric within a model.}
|
||
\label{tab:ov_over_results_by_model_parameters_conv_transformer}
|
||
\end{table}
|
||
\end{landscape}
|
||
|
||
\begin{landscape}
|
||
\begin{table}
|
||
\small
|
||
\begin{tabularx}{\linewidth}{l*{6}{X}}
|
||
\toprule
|
||
\multirow{2}{*}{Model} & \multicolumn{2}{c}{MAE} & \multicolumn{3}{c}{MSE} \\
|
||
\cmidrule(r){2-4} \cmidrule(r){5-7}
|
||
& Fertility Overall & Fertile Days & Non-Fertile Days & Fertility Overall & Fertile Days & Non-Fertile Days \\
|
||
\midrule
|
||
\multicolumn{7}{c}{\textbf{Regular Cycle Group}} \\
|
||
\midrule
|
||
LSTM & 0.026034 & 0.072578 & \textbf{0.007398} & 0.002563 & 0.007850 & \textbf{0.000415} \\
|
||
Transformer & 0.025847 & \textbf{0.065979} & 0.009693 & \textbf{0.002376} & \textbf{0.006651} & 0.000626 \\
|
||
Convolutional LSTM & 0.026750 & 0.070371 & 0.009774 & 0.002460 & 0.007313 & 0.000573 \\
|
||
Convolutional Transformer & \textbf{0.025161} & 0.066934 & 0.008789 & 0.002424 & 0.006919 & 0.000655 \\
|
||
Last-Cycle Baseline & 0.037961 & 0.085792 & 0.016133 & 0.006592 & 0.014177 & 0.003198 \\
|
||
Population Mean Baseline & 0.080743 & 0.139663 & 0.052513 & 0.016657 & 0.027671 & 0.011292 \\
|
||
User Mean Baseline & 0.032624 & 0.077341 & 0.012325 & 0.005195 & 0.011715 & 0.002312 \\
|
||
\midrule
|
||
\multicolumn{7}{c}{\textbf{Irregular Cycle Group}} \\
|
||
\midrule
|
||
LSTM & \textbf{0.036813} & 0.093108 & \textbf{0.014657} & 0.004490 & 0.013415 & \textbf{0.001076} \\
|
||
Transformer & 0.039474 & \textbf{0.084206} & 0.022359 & 0.004122 & \textbf{0.010774} & 0.001783 \\
|
||
Convolutional LSTM & 0.037182 & 0.085657 & 0.018791 & \textbf{0.004096} & 0.011188 & 0.001538 \\
|
||
Convolutional Transformer & 0.038007 & 0.086143 & 0.019175 & 0.004281 & 0.011374 & 0.001639 \\
|
||
Last-Cycle Baseline & 0.049164 & 0.124943 & 0.022958 & 0.009423 & 0.023650 & 0.004613 \\
|
||
Population Mean Baseline & 0.051673 & 0.123722 & 0.027299 & 0.009795 & 0.022330 & 0.005751 \\
|
||
User Mean Baseline & 0.054256 & 0.129195 & 0.028745 & 0.010548 & 0.023935 & 0.006159 \\
|
||
\bottomrule
|
||
\end{tabularx}
|
||
\caption{Evaluation Metrics for the fertility probability target across Different Model Architectures for the Regular and Irregular Cycle Groups.
|
||
\textbf{Bold} values represent the best values across all models for a given metric.}
|
||
\label{tab:regular_vs_irregular_fertility_results}
|
||
\end{table}
|
||
\end{landscape}
|
||
|
||
\begin{landscape}
|
||
\begin{table}
|
||
\small
|
||
\begin{tabularx}{\linewidth}{l*{6}{X}}
|
||
\toprule
|
||
\multirow{2}{*}{Model} & \multicolumn{3}{c}{MAE} & \multicolumn{3}{c}{MSE} \\
|
||
\cmidrule(r){2-4} \cmidrule(r){5-7}
|
||
& OV-Over Overall & OV-Over Before OV & OV-Over After OV & OV-Over Overall & OV-Over Before OV & OV-Over After OV \\
|
||
\midrule
|
||
\multicolumn{7}{c}{\textbf{Regular Cycle Group}} \\
|
||
\midrule
|
||
LSTM & \textbf{0.059506} & 0.087519 & \textbf{0.047856}&\textbf{0.028519}& 0.031695 & \textbf{0.026116} \\
|
||
Transformer & 0.075639 & 0.083443 & 0.069310 & 0.032929 & \textbf{0.024774} & 0.033411 \\
|
||
Convolutional LSTM & 0.076335 & 0.090550 & 0.068022 & 0.034224 & 0.028962 & 0.033929 \\
|
||
Convolutional Transformer & 0.075421 & 0.111256 & 0.060485 & 0.033333 & 0.039045 & 0.029329 \\
|
||
Last-Cycle Baseline & 0.087916 & 0.093465 & 0.074191 & 0.087916 & 0.093465 & 0.074191 \\
|
||
Population Mean Baseline & 0.285887 & \textbf{0.008876} & 0.418879 & 0.285887 & 0.008876 & 0.418879 \\
|
||
User Mean Baseline & 0.069245 & 0.069152 & 0.055724 & 0.069245 & 0.069152 & 0.055724 \\
|
||
\midrule
|
||
\multicolumn{7}{c}{\textbf{Irregular Cycle Group}} \\
|
||
\midrule
|
||
LSTM & \textbf{0.094527} & \textbf{0.063073} & \textbf{0.106232} & \textbf{0.051747} & \textbf{0.016777} & 0.070419 \\
|
||
Transformer & 0.117531 & 0.096482 & 0.116790 & 0.055990 & 0.026159 & 0.067520 \\
|
||
Convolutional LSTM & 0.108166 & 0.092732 & 0.110939 & 0.054523 & 0.030092 & \textbf{0.066643} \\
|
||
Convolutional Transformer & 0.111817 & 0.094195 & 0.114122 & 0.056075 & 0.029638 & 0.067965 \\
|
||
Last-Cycle Baseline & 0.222906 & 0.134945 & 0.258047 & 0.222906 & 0.134945 & 0.258047 \\
|
||
Population Mean Baseline & 0.171368 & 0.130366 & 0.140363 & 0.171368 & 0.130366 & 0.140363 \\
|
||
User Mean Baseline & 0.180702 & 0.088884 & 0.217144 & 0.180702 & 0.088884 & 0.217144 \\
|
||
\bottomrule
|
||
\end{tabularx}
|
||
\caption{Evaluation Metrics for the Ovulation-Over Target across Different Model Architectures for the Regular and Irregular Cycle Groups.
|
||
\textbf{Bold} values represent the best values across all models for a given metric.}
|
||
\label{tab:regular_vs_irregular_ov_over_results}
|
||
\end{table}
|
||
\end{landscape}
|
||
|