Chinese Currency Exchange Rates Forecasting with EMD-Based Neural Network

Table 7

Robust tests for comparisons with different activation functions.

	1-day	5-day	10-day	20-day	30-day
Panel A: MLP model
MLP(3)	50.06	55.17
MLP(5)	48.98
MLP(5,3)	48.14	54.81
MLP(6,4)	49.94
Panel B: EMD-MLP model
EMD(-1)-MLP(3)
EMD(-1)-MLP(5,3)
EMD(-2)-MLP(3)
EMD(-2)-MLP(5,3)
EMD(-3)-MLP(3)
EMD(-3)-MLP(5,3)
Panel C: EMD-MLP model
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 the CNY from January 2, 2006, to December 21, 2015, with a total of 2584 observations. In this table, a hyperbolic tangent function is used as the activation function. This table compares the forecasting performance, in terms of , for the MLP, EMD-MLP, and EMD-MLP models. We report as percentage for l-day ahead predictions where and 30. Moreover, we examine the ability of all models to predict the direction of change by the DAC test [40]. , , and denote statistical significance at 1%, 5%, and 10%, respectively. For each length of prediction, we mark the maximum as bold.