Forecasting import and export volume with a combined model based on wavelet filtering

Abstract

In this paper, a non-stationary time series prediction method based on wavelet transform is proposed. By wavelet decomposition, the non-stationary time series is decomposed into a low frequency signal and several high frequency signals. The high frequency signals are predicted with auto-regressive integrated moving average (ARIMA) models, and the low frequency is predicted with an improved GM(1,1)-Markov chain combined model based on Taylor approximation. Finally, an improved ARIMA-GM(1,1)-Markov chain combined model is constructed by using wavelet reconstruction. As an example, we use the statistical data of the total import and export volume in China from 2001 to 2014 for a validation of the effectiveness of the combined model.