## 4 Results
### Experimental Results  
This part of the study focuses on presenting the results obtained in terms of forecasting accuracy over the validation dataset for each dataset and each model used. The metrics used for this purpose were MAE, WAPE, MASE, MAPE and Time (T). Table 8 shows a summary of the best parameter configuration obtained for each dataset and metric, specifying the best model for each one of them. Table 9 presents a comparison between the results obtained with the different architectures for each dataset in terms of MAE. For each of the datasets, the best result is specified in bold. In quarantine period, the best result was obtained with the LSTM model, with an average error of \(3.535\,\upepsilon\). Regarding normal period, the best model was CNN, with an average error of around \(4.125\,\upepsilon\). Finally, the lowest error for the fraud period was obtained with a regression tree, resulting in an average error of \(4.034\,\upepsilon\). The last row and column of the table represent the mean MAE for each model and dataset, respectively. The best average model is LSTM and the dataset with the lowest average MAE error is the quarantine period. Figure 5: Time series of quarantine period. On the x-axis we can see that this period covers electricity prices between 15 March 2020 and 15 June 2020. The horizontal line shown indicates the average for this period, which is 28.09. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Best Model Mae** & **Best Model Mape** & **Best Model Time** \\ \hline \multirow{4}{*}{Quarantine} & STM & STM & REE \\  & Layers: 1 & Layers: 1 & Splitter: Random \\  & Units: 128 & Units: 128 & Max depth: 2 \\  & Return sequence: False & Return sequence: False & Min samples split: 2 \\  & & & Min Samples Leaf: 7 \\ \hline \multirow{4}{*}{Normal} & NN & REE & REE \\  & Layers: 3 & Splitter: Random & Splitter: Random \\  & Filters: 16 & Max depth: 8 & Max depth: 2 \\  & Pool Size: 0 & Min samples split: 4 & Min samples split: 6 \\  & & Min Samples Leaf: 5 & Min Samples Leaf: 7 \\ \hline \multirow{4}{*}{Fraud} & REE & REE & REE \\  & Splitter: Random & Splitter: Random & Splitter: Random \\ \cline{1-1}  & Max depth: 6 & Max depth: 6 & Max depth: 2 \\ \cline{1-1}  & Min samples split: 8 & Min samples split: 8 & Min samples split: 6 \\ \cline{1-1}  & Min Samples Leaf: 3 & Min Samples Leaf: 3 & Min Samples Leaf: 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Best parameter settings obtained for each dataset and each metric. For each of the possible scenarios, there is a description of which specific algorithm has obtained the best results as well as the best hyperparameters. Concerning WAPE, the results obtained can be seen in Table 10. In case of the quarantine period, the best model was LSTM with an average WAPE of 0.112. Secondly, for the normal period the best model was CNN with a percentage of 0.084. Finally, for fraud the best model was the regression tree with an average WAPE of 0.059. The last row and column of the table represent the average WAPE per model and dataset, respectively. As with MAPE, the best mean model is LSTM, while for the datasets, the best results are obtained with the fraud period. The results in terms of MASE (Table 11) follow the same pattern as for the previous two metrics. The best result for the quarantine period is obtained with the LSTM model with a value of 2.600, for the normal period the best result is 1.764 using a CNN model. Finally, for the fraud period a best result of 1.458 is obtained with decision trees. In average terms, the lowest error is obtained with the fraud period, and in terms of model with an LSTM network, as was the case for WAPE and MAE. MAPE is the only metric that differs from the others as can be seen in Table 12. In this case, for the normal period, the best score is obtained with trees, with a value of 0.095,