# GAN-Based Generation of Synthetic Data for Vehicle Driving Events
## Appendix B TimeGAN Hyperparameter Tuning
### Candidate Hyperparameters for the Evaluation Networks  
Synthetic data were generated with TimeGAN using the initial configuration in Table 4 and then evaluated by the discriminative and predictive networks, for which the initial hyperparameters are shown in Table 6. The hyperparameters calibrated for these two networks were (1) batch size and (2) number of iterations. In this group of experiments, the variation in the discriminative and predictive scores was analyzed by changing the values of the batch size and number of iterations together. In both cases, the average scores obtained with all batch sizes were calculated for each value of the specified number of iterations described below.  
In the case of the discriminative network, batch sizes of 32, 64, 128, 256, and 512 were considered, and the obtained scores were compared when running 100, 200, 250, 500, 1000, and 2000 iterations. The results of these experiments are shown in Table A1, where it can be seen that the best average score of 0.25954 was obtained when all batch sizes were considered and 200 iterations were run. Considering the values in Table A1 with 200 iterations, the batch size that provides the best average value occurs when the number of iterations is 32. Therefore, the values of 32 and 200 were selected for the batch size and number of iterations hyperparameters of the discriminative network, respectively.  
For the case of the predictive network, the same variants of the discriminative network were considered for the batch size, and the numbers of iterations considered were 500, 1000, 2000, 4000, 5000, and 8000. Table A2 shows the values found for the predictive scores, where it can be seen that the best average predictive score of 0.10597 was obtained when all batch sizes were considered and 500 iterations were executed. With 1000 iterations, a similar result is achieved; however, the lower number of iterations was preferred due to the lower computational cost. Under the same analysis as in the discriminative case, if we considered the values in Table A2 with 500 iterations, then the batch size with the best average value is obtained when the number of iterations is 256. Therefore, values of 256  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Batch**} & \multicolumn{6}{c}{**No. of Iterations**} \\ \cline{2-7}  & **100** & **200** & **250** & **500** & **1000** & **2000** & **Average** \\ \hline
32 & 0.31651 & 0.12697 & 0.19681 & 0.43482 & 0.42021 & 0.49273 & 0.331346 \\
64 & 0.15784 & 0.35663 & 0.43354 & 0.28392 & 0.41358 & 0.47525 & 0.353465 \\
128 & 0.35287 & 0.24783 & 0.41498 & 0.43118 & 0.36524 & 0.49273 & 0.384141 \\
256 & 0.36352 & 0.27920 & 0.27174 & 0.41613 & 0.41383 & 0.45427 & 0.366454 \\
512 & 0.32901 & 0.28705 & 0.28730 & 0.47678 & 0.44017 & 0.48456 & 0.384152 \\ \hline
**Average** & 0.30395 & 0.25954 & 0.32088 & 0.40857 & 0.41061 & 0.47991 & \\ \hline \hline \end{tabular}
\end{table}
Table A1: Discriminative scores by batch size and number of iterations.  
and 500 were chosen for the batch size and number of iterations hyperparameters of the predictive network, respectively. Table A3 summarizes the best average values obtained for the hyperparameters of the discriminative and predictive networks.