## 3 Results and Discussion
### Comparison of Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Dataset** & **St. Measures** & **Median** & **Mean** &
\begin{tabular}{c} **Confidence** \\ **Interval (95\%)** \\ \end{tabular} \\ \hline \multirow{8}{*}{GBR (2)} & Training & \(R^{2}\) & 0.8952 & 0.8954 & [0.8934, 0.8974] \\  & & MAE & 2.2738 & 2.2792 & [2.2533, 2.3051] \\  & & MAPE & 12.7757 & 12.8251 & [12.6434, 13.0068] \\  & & RMSE & 2.9005 & 2.9009 & [2.8671, 2.9347] \\ \cline{2-5}  & Test & \(R^{2}\) & 0.8144 & 0.8182 & [0.8078, 0.8286] \\  & & MAE & 2.9413 & 2.9209 & [2.7964, 3.0454] \\  & & MAPE & 15.9138 & 16.2251 & [15.3890, 17.0612] \\  & & RMSE & 3.8291 & 3.7959 & [3.6467, 3.9441] \\ \cline{2-5}  & All & \(R^{2}\) & 0.8765 & 0.8765 & [0.8741, 0.8789] \\  & & MAE & 2.4403 & 2.4396 & [2.4197, 2.4595] \\  & & MAPE & 13.6819 & 13.6751 & [13.5575, 13.7927] \\  & & RMSE & 3.1504 & 3.1500 & [3.1185, 3.1815] \\ \hline \multirow{8}{*}{XGBoost (2)} & Training & \(R^{2}\) & 0.8954 & 0.8960 & [0.8942, 0.8978] \\  & & MAE & 2.2936 & 2.2822 & [2.2524, 2.3120] \\  & & MAPE & 12.7911 & 12.8010 & [12.6456,12.9564] \\  & & RMSE & 2.8853 & 2.8926 & [2.8605,2.9247] \\ \cline{2-5}  & Test & \(R^{2}\) & 0.8160 & 0.8196 & [0.8111, 0.8281] \\  & & MAE & 2.8887 & 2.9020 & [2.7886, 3.0154] \\  & & MAPE & 15.9980 & 16.0921 & [15.3427, 16.8415] \\  & & RMSE & 3.8046 & 3.7812 & [3.6523, 3.9101] \\ \cline{2-5}  & All & \(R^{2}\) & 0.8773 & 0.8773 & [0.8754, 0.8792] \\  & & MAE & 2.4384 & 2.4371 & [2.4181, 2.4561] \\  & & MAPE & 13.6637 & 13.6238 & [13.4818, 13.7658] \\  & & RMSE & 3.1394 & 3.1396 & [3.1148, 3.1644] \\ \hline \multirow{8}{*}{WAE (6)} & Training & \(R^{2}\) & 0.8967 & 0.8965 & [0.8950, 0.8980] \\  & & MAE & 2.2643 & 2.2595 & [2.2340, 2.2850] \\ \cline{1-1}  & & MAPE & 12.5287 & 12.5486 & [12.3974, 12.6998] \\ \cline{1-1}  & & RMSE & 2.8769 & 2.8857 & [2.8593, 2.9121] \\ \cline{1-1} \cline{2-5}  & Test & \(R^{2}\) & 0.8185 & 0.8220 & [0.8137, 0.8303] \\ \cline{1-1}  & & MAE & 2.8447 & 2.8755 & [2.7697, 2.9813] \\ \cline{1-1}  & & MAPE & 15.7647 & 15.8056 & [15.0947, 16.5165] \\ \cline{1-1}  & & RMSE & 3.7892 & 3.7562 & [3.6275, 3.8849] \\ \cline{1-1} \cline{2-5}  & All & \(R^{2}\) & 0.8785 & 0.8782 & [0.8761, 0.8803] \\ \cline{1-1}  & & MAE & 2.4112 & 2.4135 & [2.3987, 2.4283] \\ \cline{1-1}  & & MAPE & 13.3920 & 13.3628 & [13.2487, 13.4769] \\ \cline{1-1}  & & RMSE & 3.1240 & 3.1272 & [3.0997, 3.1547] \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Comparison of GBR, XGBoost and WAE models; 10 models randomly generated with a 75:25 ratio by means of simple random sampling (see Table 15 for the description of the models). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Dataset** & \(\mathbf{R^{2}}\) & **MAE** & **MAPE** & **RMSE** & **Total Rank** & **Overall Rank** \\ \hline \multirow{2}{*}{GBR (2)} & Training & 1 & 2 & 1 & 1 & 5 & \\  & Test & 1 & 1 & 1 & 1 & 4 & 9 \\ \hline \multirow{2}{*}{XGBoost (2)} & Training & 2 & 1 & 2 & 2 & 7 & 15 \\  & Test & 2 & 2 & 2 & 2 & 8 & 1 \\ \hline \multirow{2}{*}{WAE (6)} & Training & 3 & 3 & 3 & 3 & 12 & 24 \\  & Test & 3 & 3 & 3 & 3 & 12 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Discrete ranking analysis of GBR, XGBoost and WAE models (see Table 15 for the description of the models).