Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period

Twenty-five machine learning (ML) methods and ordinary least squares regression (OLS) are trained to detect in-sample U.S. annual inflation rates up to a year in advance. The FRED-MD monthly dataset with 134 economic and financial variables from 1959 to April 2022 is used for training, validation, and forecasting. Out of these twenty-five ML methods, top-ten (by root mean square error or RMSE) are chosen to forecast the out-of-sample annual inflation rate. The ML methods are more accurate than the OLS in forecasting the annual inflation rate. The OLS does not appear in the top-10 list in any forecasting period. The ML methods robustly classify the labor market as the top factor in forecasting inflation. The labor market has a significantly higher impact on inflation than the housing or stock market.