## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **AutoKeras** & **H2O** & **MLBox** & **TPOT** & **Auto-Sklearn** \\ \hline \multicolumn{6}{c}{**MAE**} \\ \hline AutoKeras & 1.000 & 1.000 & **0.038** & **0.038** & 1.000 \\ H2O & 1.000 & 1.000 & **0.020** & **0.019** & 1.000 \\ MLBox & **0.038** & **0.020** & 1.000 & 1.000 & **0.022** \\ TOT & **0.038** & **0.019** & 1.000 & 1.000 & **0.022** \\ auto-sklearn & 1.000 & 1.000 & **0.022** & **0.022** & 1.000 \\ \hline \multicolumn{6}{c}{**RMSE**} \\ \hline AutoKeras & 1.000 & 1.000 & **0.002** & **0.040** & 1.000 \\ H2O & 1.000 & 1.000 & **0.000** & **0.008** & 1.000 \\ MLBox & **0.002** & **0.000** & 1.000 & 1.000 & **0.000** \\ TOT & **0.040** & **0.008** & 1.000 & 1.000 & **0.004** \\ auto-sklearn & 1.000 & 1.000 & **0.000** & **0.004** & 1.000 \\ \hline \multicolumn{6}{c}{**BIAS**} \\ \hline AutoKeras & 1.000 & 1.000 & **0.001** & **0.000** & 1.000 \\ H2O & 1.000 & 1.000 & **0.001** & 0.000 & 1.000 \\ MLBox & **0.001** & **0.001** & 1.000 & 1.000 & **0.000** \\ TOT & **0.000** & **0.000** & 1.000 & 1.000 & **0.000** \\ auto-sklearn & 1.000 & 1.000 & **0.000** & **0.000** & 1.000 \\ \hline \multicolumn{6}{c}{**NSEI**} \\ \hline AutoKeras & 1.000 & 0.835 & **0.000** & **0.001** & 1.000 \\ H2O & 0.835 & 1.000 & **0.030** & **0.046** & 1.000 \\ MLBox & **0.000** & **0.030** & 1.000 & 1.000 & **0.007** \\ TOT & **0.001** & **0.046** & 1.000 & 1.000 & **0.013** \\ auto-sklearn & 1.000 & 1.000 & **0.007** & **0.013** & 1.000 \\ \hline \multicolumn{6}{c}{**KGEI**} \\ \hline AutoKeras & 1.000 & 1.000 & **0.043** & **0.014** & 1.000 \\ H2O & 1.000 & 1.000 & **0.004** & **0.001** & 1.000 \\ MLBox & **0.043** & **0.004** & 1.000 & 1.000 & **0.011** \\ TOT & **0.014** & **0.001** & 1.000 & 1.000 & **0.003** \\ auto-sklearn & 1.000 & 1.000 & **0.011** & **0.003** & 1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The computed \(p\)-values with the Dunn test. Pairwise computed \(p\)-values are reported for each couple of AutoML models and performance metrics. For the sake of readability, the \(p\)-values less than the chosen significance level \(\alpha=0.05\) are reported as bold and underlined text. performance metric to compare performance variances on the same metric scale gained when leveraging each input feature. As a result, the performed tests showed no statistically significant differences in-group variances for all the employed metrics, as all the computed \(p\)-values were greater than the significance level \(\alpha=0.05\). In particular, the \(p\)-values found by the executed Fligner-Killeen tests ranged within \(0.146\leq p\leq 0.884\). The Kruskal-Wallis was finally executed, with test groups defined by grouping the obtained model results by feature kind and performance metric. In particular, a different Kruskal-Wallis test was performed for each performance metric to compare performance medians on the same metric scale, gained when leveraging each employed feature. As a final result, the Kruskal-Wallis test indicated no statistically significant differences in the AutoML model median performances with respect to the employed features, as all the computed \(p\)-values were greater than the chosen significance level \(\alpha=0.05\). In particular, the \(p\)-values found by the executed Kruskal-Wallis tests ranged between \(0.448\leq p\leq 0.996\) for all the analyzed metrics. Therefore, post hoc pairwise Dunn tests were not executed. Furthermore, the forecasting performances of all the possible combinations of AutoML models and input features were ranked by providing them a performance score: The \(\text{AutoML}_{\text{score}}(m,f,l)\) was calculated for each combination of AutoML model \(m\), input feature \(f\), and time-lag \(l\), by taking the sum of the absolute differences between the test set performance of the AutoML model \(m\) and the optimum value for each employed performance metric (value 0 for MAE, RMSE, and BIAS; value 1 for NSEI and KGEI).