Forecasting the Cost of the Basic Family Food Basket in the Districts of Panama and San Miguelito: A Comparative Analysis of Statistical and Machine Learning Models

Forecasting food costs is an important tool for food security planning and management. This study aimed to evaluate and compare the performance of statistical and machine learning models for forecasting the monthly cost of the Basic Family Food Basket (BFFB) in Panama and San Miguelito. Monthly data from 2009 to 2024 were chronologically divided into training and test sets. A Naïve model was used as a benchmark, while ARIMA, Holt, Random Forest and XGBoost models were evaluated using MAE, RMSE and MAPE. The main evaluation used a fixed-origin multi-step forecasting scheme, complemented by a rolling-origin evaluation. Predictive accuracy was also assessed using the Diebold–Mariano test with Holm correction for multiple comparisons. Random Forest yielded the lowest test-set errors (MAE = 13.82, RMSE = 17.23, MAPE = 3.92%), followed by XGBoost (MAE = 15.84, RMSE = 18.14, MAPE = 4.53%). The rolling-origin evaluation also showed favorable performance for both machine learning models, while the Diebold–Mariano test identified statistically significant differences in four of the ten pairwise comparisons based on the Holm p-values, all involving XGBoost. These results highlight the potential of machine learning models for BFFB cost forecasting and support their usefulness as tools for food security-related monitoring and planning.