Exchange Rate Forecast Model Based on Prediction-Revise Method
Feng Shan, Ningsheng Gong · Jisuanji gongcheng · 2004
Neural network is a powerful tool for forecasting financial time series, but several design factors significantly impact the result accuracy. These factors include selection of input variables, architecture of the network, and quantity of training data. In this paper, a neural network forecast model based on the prediction-revise method is presented and the effects on different sizes of training sample sets on forecasting exchange rates are examined. The results show that the forecast effect on big training sample set is better than that on small set and the predication accuracy of the proposed approach is generally better than that of individual neural networks.