Efficient financial time series forecasting model using DWT decomposition

This paper proposes an efficient time series fore- casting model for exchange rates. Previous literature reveals that Functional Link Artificial Neural Network (FLANN) is very effective in financial time series forecasting involving less computational load and fast forecasting capability. Autoregressive Integrated Moving Average (ARIMA) models are well known for their remarkable forecasting accuracy. In this literature, we have used Discrete Wavelet Transform (DWT) to decompose the in-sample training data into linear (detailed) and nonlinear (approximate) components, then applied ARIMA and FLANN model to forecast the respective components. The proposed method amalgamate the unique strengths of ARIMA, FLANN and DWT to improve the forecasting accuracy of a financial time series data. Simulation results show superiority of the proposed method.