Affiliations: [a]
Department of Security, Hebei University, Baoding, P.R. China | [b]
College of Economics, Hebei University, Baoding, P.R. China | [c]
College of Physics and Technology, Hebei University, Baoding, P.R. China
Abstract: The autoregressive integrated moving average (ARIMA)–backpropagation (BP) integrated intelligent algorithm for consumer price index (CPI) forecasting was designed based on the ARIMA intelligent forecasting method and the BP intelligent neural network algorithm. The irregular variations in CPI time series data were divided into linear and nonlinear variations. The linear variation was fitted by the ARIMA intelligent forecasting method, and the nonlinear variation was fitted by the BP intelligent neural network. The sum of the fitted linear and nonlinear variations was the CPI forecasted by the ARIMA-BP integrated intelligent algorithm. Results demonstrated that the ARIMA-BP integrated intelligent algorithm could achieve high-precision fitting of the historical CPI data of China. The proposed algorithm showed a forecast error that was smaller than that of the single ARIMA model. Owing to the complexity of the CPI and the combined influence of various factors, achieving accurate CPI forecast is difficult. Such a new integrated intelligent algorithm provides a referent scientific method to forecast the CPI of China in the future. The results can provide government departments reference information of timely price control.
