Modelling on Car-Sharing Serial Prediction Based on Machine Learning and Deep Learning

Table 12

Machine learning for combined features evaluation results.

	MAE	MSE	RMSE	MAPE	RMSLE
XGBoost	0.06984	0.00849	0.09212	0.10335	0.06984
VAR	1.0498	1.17777	1.47572	0.39307	0.3548
SVM	1.08607	1.59434	1.66544	1.78393	1.12886
KNN	1.18772	2.08258	1.7107	1.89279	1.44327
(a) Class A
	MAE	MSE	RMSE	MAPE	RMSLE
XGBoost	0.08978	0.0223	0.14396	0.11954	0.0498
VAR	0.44327	0.36056	0.60046	0.13314	0.08978
SVM	0.7216	1.03716	1.01919	1.14185	0.61201
KNN	0.76191	1.41458	1.18536	1.23693	0.72143
(b) Class B
	MAE	MSE	RMSE	MAPE	RMSLE
XGBoost	0.11671	0.03406	0.18643	0.25127	0.11671
VAR	0.43631	0.33916	0.58238	0.38869	0.43631
SVM	0.80446	0.97407	0.98695	1.12311	0.27791
KNN	0.839	1.86212	1.36414	1.22258	0.53914
(c) Class C
	MAE	MSE	RMSE	MAPE	RMSLE
XGBoost	0.01955	0.00291	0.05102	0.02952	0.0295
VAR	0.43306	0.34416	0.58665	0.15809	0.43306
SVM	0.54572	0.69482	0.83307	1.16535	0.54581
KNN	0.61314	1.14905	1.071	1.18552	0.43926
(d) Class D
	MAE	MSE	RMSE	MAPE	RMSLE
XGBoost	0.01691	0.00204	0.04636	0.00572	0.01591
VAR	0.08465	0.08488	0.29134	0.13918	0.07416
SVM	0.1173	0.11817	0.34376	0.52012	0.0847
KNN	0.27281	0.13251	0.16539	1.94874	0.17384
(e) Class E