Hybrid ARIMA-BPNN model for time series prediction of the Chinese stock market

Stock price prediction is a challenging task owing to the complexity patterns behind time series. Autoregressive integrated moving average (ARIMA) model and back propagation neural network (BPNN) model are popular linear and nonlinear models for time series forecasting respectively. The integration of two models can effectively capture the linear and nonlinear patterns hidden in a time series and improve forecast accuracy. In this paper, a new hybrid ARIMA-BPNN model containing technical indicators is proposed to forecast four individual stocks consisting of both main board market and growth enterprise market in software and information services sector. Experiment results show that the proposed method achieves the better one-step-ahead forecasting accuracies namely 78.79%, 72.73%, 59.09% and 66.67% respectively for each series than those of ARIMA, BPNN, and Khashei and Bijari's hybrid models.