Mixed-Frequency Machine Learning for Nowcasting Energy-Related CPI in Turkey: Evidence from Explainable Models

This study examines whether the monthly inflation rate of the energy-related consumer price index component in Turkey can be nowcast more accurately with mixed-frequency indicators. An expanding-window pseudo-out-of-sample design is used to compare a seasonal naive benchmark with Elastic Net, XGBoost, and LightGBM. The predictor set combines monthly macroeconomic indicators with features derived from daily Brent oil prices, daily USD/TRY exchange rates, and hourly EPİAŞ day-ahead electricity market data for 2012–2025. Forecast performance is evaluated with root mean squared error, mean absolute error, and symmetric mean absolute percentage error, while core-sample forecast differentials are assessed with the Diebold–Mariano test. All machine-learning models outperform the benchmark, and the lowest forecast errors are obtained from the core XGBoost specification. Explainability results from standardized Elastic Net coefficients and SHAP decompositions show that headline inflation and EPİAŞ variables provide the largest share of predictive content, Brent forms a secondary cost channel, inflation expectations are supportive, and exchange-rate variables do not emerge as an independently dominant block. The results support mixed-frequency machine learning as a useful framework for short-run monitoring of energy-related inflation in Turkey.