Short-Term Speed Prediction Using Remote Microwave Sensor Data: Machine Learning versus Statistical Model

Table 1

Prediction accuracy of models for different forecasting steps ahead in station A.

MAE	Number of forecasting steps ahead
	1	3	5	10
BPNN	2.5027	3.0243	3.4576	4.3553
NARXNN	2.5041	3.0232	3.4482	4.3357
SVM-RBF	2.6537	3.1135	3.4858	4.2625
SVM-LIN	2.7492	3.1160	3.4714	4.2604
MLR	3.0820	3.4795	3.8774	4.6338
ARIMA	2.6777	3.1501	3.5291	4.3233
VAR	2.6835	3.2931	3.7145	4.6030
ST	2.9398	3.1414	3.4932	4.2355
MAPE (%)	Number of forecasting steps ahead
	1	3	5	10
BPNN	5.2831	6.4927	7.5472	9.5735
NARXNN	5.2923	6.4995	7.4855	9.6267
SVM-RBF	5.5839	6.6952	7.5901	9.4775
SVM-LIN	5.2878	6.5408	7.6922	9.4937
MLR	5.9048	7.3508	8.1869	9.9782
ARIMA	5.6290	6.7794	7.6974	9.6376
VAR	5.6271	6.7202	8.2556	9.5937
ST	6.1660	6.7725	7.6390	9.5080