Analysis and Forecast of CPI in China Based on LSTM and VAR Model

Artificial neural network (ANN) is a prevalent tool because of its extensive adaptivity and outstanding performance. According to previous studies, Long Short-Term Memory (LSTM) neural networks generally perform well in forecasting financial time series than other models. However, few studies apply LSTM to CPI and price level forecasting. This paper separately constructs the LSTM and the Vector Autoregression (VAR) model, a classic econometric approach for time series forecasting, based on 23 factors that affect CPI directly or indirectly. The results show that the error of the LSTM is significantly lower than that of the VAR in forecasting China’s CPI, while the VAR model provides an explicit explanation of the factors of CPI forecasting through the Granger causality test. Additionally, a synthetic model combining the advantages of both generates a more satisfying outcome. This paper forecasts the CPI by combining the LSTM and VAR models for the first time and provides a new reference to the inflation forecasting area.