## 4 Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **LSTM** & **CNN** & **TCN** & **MLP** & **TREE** & **RF** & **Average** \\ \hline Quarantine & **0.112** & 0.134 & 0.127 & 0.116 & 0.129 & 0.142 & 0.127 \\ \hline Normal & 0.086 & **0.084** & 0.102 & 0.085 & 0.085 & 0.085 & 0.088 \\ \hline Fraud & 0.065 & 0.072 & 0.083 & 0.071 & **0.059** & 0.072 & 0.071 \\ \hline Average & 0.088 & 0.097 & 0.104 & 0.091 & 0.091 & 0.009 & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: WAPE results for each period and each model. The results have been obtained after choosing the best set of parameters for each case by carrying out a parameter optimization process. The last row and column correspond to the means for each model and dataset, respectively. The best model for each dataset is specified in bold. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **LSTM** & **CNN** & **TCN** & **MLP** & **TREE** & **RF** & **Average** \\ \hline Quarantine & **2.600** & 3.080 & 2.988 & 2.693 & 2.979 & 3.237 & 2.929 \\ \hline Normal & 1.801 & **1.764** & 2.130 & 1.793 & 1.769 & 1.775 & 1.839 \\ \hline Fraud & 1.610 & 1.737 & 2.009 & 1.732 & **1.458** & 1.749 & 1.716 \\ \hline Average & 2.004 & 2.194 & 2.376 & 2.073 & 2.069 & 2.254 & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: MASE results for each period and each model. The results have been obtained after choosing the best set of parameters for each case by carrying out a parameter optimization process. The last row and column correspond to the means for each model and dataset, respectively. The best model for each dataset is specified in bold. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **LSTM** & **CNN** & **TCN** & **MLP** & **TREE** & **RF** & **Average** \\ \hline Quarantine & **3.535** & 4.199 & 3.997 & 3.648 & 4.063 & 4.414 & 3.976 \\ \hline Normal & 4.212 & **4.125** & 4.991 & 4.192 & 4.160 & 4.152 & 4.305 \\ \hline Fraud & 4.444 & 4.847 & 5.606 & 4.834 & **4.034** & 4.881 & 4.774 \\ \hline Average & 4.064 & 4.391 & 4.865 & 4.224 & 4.085 & 4.482 & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: MAE results for each period and each model. The results have been obtained after choosing the best set of parameters for each case by carrying out a parameter optimization process. The last row and column correspond to the means for each model and dataset, respectively. The best model for each dataset is specified in bold. while for quarantine and fraud the best result is still LSTM and Trees, with 0.126 and 0.059, respectively, as was the case for the three previous metrics. Considering the four tables together, it can be observed that the same patterns are repeated in most cases, i.e., for each period the best model coincides for MAE, WAPE, MASE and MAPE. If the average of these measures with respect to the datasets is taken into account, the influence of each of the datasets on the final result is clearly evidenced. The most characteristic case is the fraud period, which had on average a higher price than the other two periods, resulting in a higher average MAE. However, for the case of WAPE, the fraud period is the one that achieves a lower average WAPE, which is due to the fact that this metric is divided by the mean of the values, so the higher the mean, the lower the WAPE. Something similar happens with MASE and MAPE, as it can be observed that for the fraud period the values are lower with respect to the other two periods, this is due to the fact that they are metrics inversely proportional to the values of the time series. Table 13 shows the average training time for each of the cases presented in this work. If we take into account the data sets, there is not much difference between the training, since the length of the time series is the same. In contrast, the average time taken for each model is significantly different from one model to the other, with the tree model being the fastest and the LSTM networks the slowest.