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

Table 6

Machine learning for univariate time series evaluation results.

	MAE	MSE	RMSE	MAPE	RMSLE
XGBoost	0.07272	0.00975	0.09868	0.30398	0.07189
VAR	1.06025	1.20552	1.4851	0.98598	1.15297
SVM	1.15297	1.86949	1.69395	1.7977	1.3575
KNN	1.19834	2.77452	1.77467	1.91974	1.45087
(a) Class A
	MAE	MSE	RMSE	MAPE	RMSLE
XGBoost	0.09125	0.02252	0.14942	0.18352	0.06025
VAR	0.45087	0.36788	0.60653	0.14527	0.09057
SVM	0.72289	1.04231	1.01927	1.14777	0.960432
KNN	0.76353	1.48786	1.18645	1.25337	0.72234
(b) Class B
	MAE	MSE	RMSE	MAPE	RMSLE
XGBoost	0.1179	0.03489	0.18489	0.29007	0.1179
VAR	0.43931	0.34352	0.5861	0.50319	0.43931
SVM	0.81499	0.99441	0.99721	1.1251	0.59046
KNN	0.83915	1.86306	1.36483	1.22767	1.30815
(c) Class C
	MAE	MSE	RMSE	MAPE	RMSLE
XGBoost	0.03654	0.00314	0.05391	0.04028	0.03299
VAR	0.43459	0.34597	0.58819	0.18361	0.43459
SVM	0.54589	0.69401	0.83355	1.16683	0.54795
KNN	0.61745	1.14995	1.07216	1.18775	0.90985
(d) Class D
	MAE	MSE	RMSE	MAPE	RMSLE
XGBoost	0.04051	0.00285	0.05338	0.00683	0.04051
VAR	0.08543	0.08782	0.29143	0.14278	0.08659
SVM	0.14529	0.11864	0.34444	0.53728	0.08543
KNN	0.27331	0.13296	0.56446	1.9495	0.47325
(e) Class E