Forecasting exchange rate using EMD and BPNN optimized by particle swarm optimization

Abstract

This study applied back-propagation neural network (BPNN) and empirical mode decomposition (EMD) techniques and optimized the hybrid model by particle swarm optimization (PSO) to forecast exchange rate. The aim of this study is to examine the feasibility of the proposed EMD-BPNN-PSO model in exchange rate forecasting. In the first stage, the original exchange rate series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). In the second stage, kernel predictors such as BPNN are constructed for forecasting. Compared with traditional model (random walk), the proposed model performs best. The mean absolute percentage errors are significantly reduced. © 2011 AICIT.