Application of SARIMA Model: Forecasting CPI and PPI Inflation Rates in the U.S.

This research seeks to improve the precision of future inflation rate forecasts through a comprehensive analysis of the time series data of the U.S. Consumer Price Index (CPI) and Producer Price Index (PPI). This holds significant reference value for the government when formulating economic policies, monetary policies, and fiscal decisions, especially when facing economic fluctuations or external shocks, as it can provide more accurate inflation expectations, thereby enabling the formulation of more forward-looking regulatory measures. The data for this research is sourced from the FRED database, which contains detailed historical CPI and PPI data. This research utilized the traditional SARIMA model for time series analysis and performed thorough data preprocessing and trend analysis to guarantee the stability and predictability of the data. The results indicate that the SARIMA model exhibits different effects in capturing the trend changes of CPI and PPI inflation rates: first, the SARIMA model demonstrates high accuracy in identifying and predicting the long-term trend of CPI; second, it shows stronger adaptability and accuracy in short-term predictions of PPI change rates. This indicates that different economic indicators respond differently to the model. In the future, it may be worth considering the introduction of other advanced forecasting models, such as machine learning algorithms and hybrid models, to further enhance the predictive capability regarding changes in the inflation rate, thereby providing policymakers with more precise data support.