Exchange Rate Prediction through ANN Based on Kernel Regression

This paper represents a fusion model of functional link artificial neural network (FLANN) based on Kernel Regression (KR) for modeling and prediction of exchange rate time series. To predict the exchange rate, we process the exchange rate datasets with KR to smooth the noise. And then the smoothed datasets are nonlinearly expanded using the sine and cosine expansions before inputting to the FLANN model. Using exchange rates between US to British Pound, Indian Rupees and Japanese Yen, we conducted several experiments on exchange rate prediction. We compare the performance to the FLANN model without KR and to the adaptive exponential smoothing method (AES), and it is observed that the FLANN-KR model outperforms the two other methods.