Chinese Currency Exchange Rates Forecasting with EMD-Based Neural Network

Table 8

NMSE comparisons for CNY and CNH.

	1-day	5-day	10-day	20-day	30-day
Panel A: CNY
EMD(-1)-MLP(5,3)			4.9711	11.4496	14.8922
EMD(-2)-MLP(5,3)	0.3417			8.4836	8.5811
EMD(-3)-MLP(5,3)	0.6015				7.2250
EMD(0)-MLP(3)
EMD(0)-MLP(5,3)
EMD(-1)-MLP(3)
EMD(-1)-MLP(5,3)
EMD(-2)-MLP(3)	0.2847
EMD(-2)-MLP(5,3)	0.2960
Panel B: CNH
EMD(-1)-MLP(5,3)		2.2893	6.4050	12.8439	20.9652
EMD(-2)-MLP(5,3)				12.2240	22.9451
EMD(-3)-MLP(5,3)	0.9658				19.4264
EMD(0)-MLP(3)
EMD(0)-MLP(5,3)
EMD(-1)-MLP(3)
EMD(-1)-MLP(5,3)
EMD(-2)-MLP(3)
EMD(-2)-MLP(5,3)

Consider both the CNY and CNH from January 3, 2011, to December 21, 2015, with a total of 1304 observations. This table compares the forecasting performance, in terms of the NMSE, for several forecasting models. We report the NMSE as percentage for l-day ahead predictions where and 30. The DM test [38] is used to compare the forecast accuracy of EMD-MLP (EMD-MLP) model and the corresponding MLP model. , , and denote statistical significance at 1%, 5%, and 10%, respectively. For each length of prediction, we mark the minimum NMSE as bold.