Selecting Wavelet Transforms Model in Forecasting Financial Time Series Data Based on ARIMA Model

Recently, wavelet transforms have gained very high attention in many fields
and applications such as physics, engineering, signal processing, applied
mathematics and statistics. In this paper, we present the advantage of wavelet
transforms in forecasting financial time series data. Amman stock market
(Jordan) was selected as a tool to show the ability of wavelet transform in
forecasting financial time series, experimentally. This article suggests a novel
technique for forecasting the financial time series data, based on Wavelet
transforms and ARIMA model. Daily return data from 1993 until 2009 is used
for this study.