Predictive Analytics, Artificial Intelligence, and Machine Learning for Real-Time Nowcasting and Forecasting of Inflation Using High-Frequency Retail Price and Energy Market Data

Abstract

Inflation forecasting became harder after the pandemic because price formation turned faster, more uneven, and more exposed to energy shocks, supply disruptions, and changes in household spending. This paper develops a framework for real-time nowcasting and short-horizon forecasting of U.S. inflation by combining predictive analytics, artificial intelligence, and machine learning with high-frequency retail price and energy-market information observed before official CPI release dates. The empirical design uses publicly available data, centered on BLS consumer price and average-price series and EIA daily and weekly energy prices. Monthly CPI inflation is linked to within-month signals from retail gasoline prices, electricity and utility-gas measures, weekly retail motor fuel prices, and daily crude-oil benchmarks. The modeling architecture integrates econometric benchmarks with elastic net, random forest, and gradient boosting models under pseudo-real-time evaluation. Results from the empirical illustration show that feature-rich machine learning models, especially penalized regression that controls overfitting while exploiting mixed-frequency signals, can outperform naïve and autoregressive benchmarks for 2023-2024 monthly nowcasts. The findings also indicate that gasoline and broader energy signals are most valuable when inflation is turning and when conventional lag structures respond too slowly. Beyond predictive gains, the paper contributes a transparent workflow for mixed-frequency feature engineering, model governance, and explainability. High-frequency inflation surveillance should not replace official statistics or professional judgment; it should complement them by offering earlier, more adaptive evidence for central banks, financial institutions, retailers, and policy analysts. Practical implications and future research directions for richer scanner and web-scraped price systems are discussed.