further work on results
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-11
@@ -70,18 +70,51 @@ Additionally, model performance is compared to the three baseline models introdu
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\caption{Performance of LSTM models with different input window lengths and fixes input resolutions for the fertility probability target}
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\label{fig:results_performance_lstm_fertility_input_window_length}
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\end{figure}
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\begin{tabular}{lrrrrrrr}
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\toprule
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{} & MAE Fertility Overall & MAE Fertile Days & MAE Non-Fertile Days & MSE Fertility Overall & MSE Fertile Days & MSE Non-Fertile Days & Input-Window-Length in Days \\
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\midrule
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4 & 0.0378 & 0.0834 & 0.0192 & 0.0043 & 0.0102 & 0.0019 & 160 \\
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5 & 0.0399 & 0.0857 & 0.0212 & 0.0045 & 0.0105 & 0.0021 & 10 \\
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11 & 0.0421 & 0.0994 & 0.0180 & 0.0052 & 0.0144 & 0.0013 & 20 \\
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13 & 0.0420 & 0.1006 & 0.0176 & 0.0053 & 0.0147 & 0.0014 & 80 \\
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14 & 0.0406 & 0.0938 & 0.0184 & 0.0049 & 0.0128 & 0.0016 & 40 \\
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\bottomrule
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\end{tabular}
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\begin{landscape}
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\begin{table}
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% \begin{tabular}{lrrrrrrr}
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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{Convolution-LSTM}} \\ \midrule
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10 & 0.0435 & \underline{0.0954} & 0.0224 & 0.0050 & 0.0133 & 0.0017 \\
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20 & \underline{0.0414} & 0.0996 & \underline{0.0179} & 0.0050 & 0.0145 & \underline{\textbf{0.0013}} \\
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40 & 0.0394 & 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{Convolution-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{tabular}
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\end{tabularx}
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\caption{Evaluation Metrics for the Fertility-Probability target for each model architecture across different input lenghts.
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Underlined values indicate the best value per metric for a model and bold and underlined indicate global best values for a metric.}
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\label{tab:fertility_results}
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\end{table}
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\end{landscape}
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For the LSTM model, the error metrics show a small improvement with larger input window size.
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Also, the MSE metric is numerically smaller than the MAE, which suggests, that the errors are rather small and tightly clustered with few large outliers.
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@@ -123,6 +156,52 @@ However, there is no consistent upward or downward trend when changing the input
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\subsubsection{Comparison with Baselines}\label{subsubsec:fert_comparison_with_baselines}
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\subsection{Ovulation-Over Prediction}\label{subsubsec:ov_over_prediction}
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\begin{landscape}
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\begin{table}
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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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& OV-Over Overall & OV-Over Before OV & OV-Over After OV & OV-Over Overall & OV-Over Before OV & OV-Over After OV \\
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\midrule
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\multicolumn{7}{c}{\textbf{LSTM}} \\
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\midrule
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10 & 0.1218 & 0.1044 & 0.1223 & \underline{0.0612} & 0.0289 & 0.0711 \\
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20 & 0.1153 & \underline{\textbf{0.0745}} & 0.1312 & 0.0641 & \underline{\textbf{0.0212}} & 0.0822 \\
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40 & 0.1066 & 0.0820 & 0.1128 & 0.0616 & 0.0281 & 0.0740 \\
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80 & 0.1173 & 0.0842 & 0.1291 & 0.0647 & 0.0263 & 0.0801 \\
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160 & \underline{0.1039} & 0.1139 & \underline{0.0973} & 0.0557 & 0.0462 & \underline{0.0580} \\
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\midrule
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\multicolumn{7}{c}{\textbf{Transformer}} \\
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\midrule
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10 & 0.1137 & 0.1006 & 0.1186 & 0.0618 & 0.0336 & 0.0740 \\
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20 & 0.1204 & 0.0771 & 0.1366 & 0.0690 & \underline{0.0255} & 0.0864 \\
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40 & \underline{\textbf{0.1017}} & 0.1409 & \underline{\textbf{0.0883}} & \underline{\textbf{0.0533}} & 0.0621 & \underline{\textbf{0.0520}} \\
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80 & 0.1138 & \underline{0.0897} & 0.1234 & 0.0654 & 0.0356 & 0.0788 \\
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160 & 0.1076 & 0.0959 & 0.1141 & 0.0606 & 0.0379 & 0.0714 \\
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\midrule
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\multicolumn{7}{c}{\textbf{Convolution-LSTM}} \\
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\midrule
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10 & 0.1847 & 0.1878 & 0.1840 & 0.0819 & 0.0581 & 0.0949 \\
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20 & 0.1493 & 0.1274 & 0.1562 & \underline{0.0699} & \underline{0.0389} & \underline{0.0833} \\
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40 & \underline{0.1455} & \underline{0.1089} & \underline{0.1561} & 0.0722 & 0.0358 & 0.0852 \\
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80 & 0.2327 & 0.1796 & 0.2507 & 0.1168 & 0.0686 & 0.1327 \\
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160 & 0.2518 & 0.2040 & 0.2757 & 0.1293 & 0.0878 & 0.1503 \\
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\midrule
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\multicolumn{7}{c}{\textbf{Convolution-Transformer}} \\
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\midrule
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10 & 0.1472 & 0.1053 & 0.1651 & 0.0768 & 0.0317 & 0.0966 \\
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20 & 0.1530 & 0.1307 & 0.1637 & 0.0745 & 0.0443 & 0.0886 \\
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40 & \underline{0.1448} & 0.1228 & \underline{0.1514} & \underline{0.0709} & 0.0435 & \underline{0.0820} \\
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80 & 0.1685 & 0.1164 & 0.1889 & 0.0865 & 0.0345 & 0.1089 \\
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160 & 0.2440 & \underline{0.1051} & 0.3117 & 0.1403 & \underline{0.0286} & 0.1946 \\
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\bottomrule
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\end{tabularx}
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\caption{Evaluation Metrics for the Ovulation-Over target for each model architecture across different input lenghts.
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Underlined values indicate the best value per metric for a model and bold and underlined indicate global best values for a metric.}
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\label{tab:ov_over_results}
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\end{table}
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\end{landscape}
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\subsubsection{Performance around Ovulation}\label{subsubsec:ov_over_performance_around_ovulation}
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