Abstract: The CPI index plays an important role in monitoring and managing the operation of a country’s macro economy, and the timely and effective prediction of its future trend is conducive for the country to take reasonable regulatory measures in time. Therefore, starting from the price determination theory, this paper constructs a logical framework for the relationship between commodity price volatility and individual web search behavior, then it establishes a mixed-frequency data sampling model (MIDAS) for monthly CPI prediction by using the daily frequency data of network search index. The findings show that: (1) the prediction method using web searching high frequency data can improve the accuracy of both the inside matching and outside prediction of CPI samples; (2) the high frequency data using web search can improve the success rate of capturing the“inflection point”in the CPI trend; (3) the high frequency data using web searching can provide the value of CPI forecasting with higher prediction accuracy approximately half a month before the official release of CPI data.

Key words: Internet searching big data; MIDAS model; CPI; short-term forecasting; prediction of inflection point