A Comparative Evaluation of Forecasting Techniques for Public Sector Food Prices: From Statistical to Modern Methods
This paper examines the performance of traditional statistical models (SARIMA & SARIMAX) and modern machine learning models (XGBoost) for forecasting Swedish public sector food prices organized by a hierarchical index structure. The depth of the analysis is made possible by collaborating with Matilda Foodtech, who gave access to anonymized food procurement data from Swedish public organization