MODEL-FREE FORECASTING FOR NONLINEAR TIME-SERIES (WITH APPLICATION TO EXCHANGE-RATES)
The first step in nonlinear model construction is data identification. While the major drawback with most of the nonlinear tests is that they are designed to test a parametric null against certain parametric alternatives and are therefore inconsistent against all possibile alternatives. In this paper, by incorporating the neurocomputing technique, the intriguing model construction and forecast problems such as how to find a best fitted model and to obtain a more appropriate prediction performance are addressed. The dynamic abilities of this approach lies in the fact that the multilier feed forward networks are functional approximators for any functions. Simulations verify that the neural networks perform a robust forecast for some nonlinear time series. Finally, we use the data of the exchange rates of Taiwan to compare the predictive performances of neural networks with ARIMA models.
model-free forecasting neural networks exchange rates backpropagation method bilinear models