Table 3 The summary of statistical analysis.

Model	Run Time	R-squared			MAPE			MAE			MSE
Model	Run Time	Training	Validation	Testing	Training	Validation	Testing	Training	Validation	Testing	Training	Validation	Testing
SVM	0.0094	0.9579	0.9562	0.9593	16.3489	16.6465	40.6545	0.0407	0.0408	0.0361	0.0024	0.0024	0.0021
MLP	10.6822	0.9506	0.9447	0.9455	15.1776	18.2716	24.8411	0.0437	0.0458	0.0407	0.0029	0.0030	0.0028
CNN	15.7672	0.9855	0.9841	0.9773	7.4173	7.2048	21.8950	0.0229	0.0248	0.0270	0.0008	0.0008	0.0011
Ensemble learning	0.0217	0.9953	0.9917	0.9934	5.4496	6.2881	15.7982	0.0135	0.0163	0.0141	0.0002	0.0004	0.0003
RF	0.0407	0.9992	0.9982	0.9982	1.5013	2.6194	4.5270	0.0048	0.0081	0.0073	0.0000	0.0001	0.0001
KNN	0.0005	1.0000	0.9798	0.9880	0.0000	5.8111	5.9151	0.0000	0.0191	0.0149	0.0000	0.0011	0.0006
DT	0.0163	1.0000	0.9988	0.9985	0.0000	2.8254	4.6935	0.0000	0.0071	0.0077	0.0000	0.0001	0.0001
Adaptive boosting	0.2623	0.9574	0.9520	0.9477	16.3788	19.3579	36.6926	0.0419	0.0441	0.0442	0.0025	0.0026	0.0027

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