Financial time series prediction using wavelet and artificial neural network

Abstract

This paper focuses on the modelling of financial time series using the coupling of the discrete wavelet transform and nonlinear autoregressive neural network. This hybrid modelling method is based on the use of decomposed time series using the discrete wavelet transform as inputs to the artificial neural networks. This method has been applied to three financial series (exchange rate EUR/USD, the Brent price and NASDAQ composite price). The simulation results using R software show the robustness of the proposed model compared to other modelling methods applied to the same financial time series.