Forecasting Air Passenger Traffic by Support Vector Machines with Ensemble Empirical Mode Decomposition and Slope-Based Method

Table 1

Forecasting performances of all models across all the data series.

		EEMD-Slope-SVMs	EEMD-SVMs	SVMs	Holt-Winters	ARIMA
	MAPE	1.501	1.8332	2.096	5.7316	3.1582
American	RMSE	127192	141840	161874	449709	251254
	GMRAE	0.2165	0.2973	0.3351	0.8012	0.5268
	DS	1	1	1	0.6087	0.739
	MAPE	3.815	4.552	5.4158	7.4413	6.9646
Delta	RMSE	276491	308793	331704	403902	408285
	GMRAE	0.5018	0.5216	0.7141	0.8618	1.1068
	DS	0.8261	0.7391	0.7261	0.6957	0.6522
	MAPE	1.1369	1.2937	1.2956	5.8553	6.0091
Southwest	RMSE	150190	150777	151706	696873	632114
	GMRAE	0.208	0.251	0.2643	0.8126	1.1055
	DS	0.936	0.927	0.913	0.695	0.522
	MAPE	1.3901	1.9475	2.1505	6.5151	4.0872
United	RMSE	96132	106727	120961	393550	251327
	GMRAE	0.2091	0.2963	0.3811	1.1877	0.801
	DS	0.9774	0.9565	0.913	0.608	0.913
	MAPE	2.017	2.9321	3.8019	5.5213	5.6763
easyJet	RMSE	74361	81931	102859	175269	175404
	GMRAE	0.4401	0.5183	0.6872	0.526	0.525
	DS	0.9107	0.8696	0.78261	0.6087	0.6075
	MAPE	1.4078	1.8766	2.7149	4.1953	3.659
Virgin	RMSE	10191	11150	14150	22395	18455
	GMRAE	0.4109	0.4655	0.5412	0.6303	0.6515
	DS	0.8991	0.8696	0.7826	0.6522	0.7391