A White-box Approach to Forecasting Petrol Prices in Import-Dependent Economies base on Machine Learning and Explainable Artificial Intelligence

Abstract Fuel price volatility presents significant economic and policy challenges in import-dependent economies such as Zambia. This study implements and compares four machine learning models: Ridge Linear Regression, Random Forest, Support Vector Regression and Long Short-Term Memory, using the MAE, RMSE, R² and DM statistic as the performance evaluation metrics. The dataset used in this research consisted of the daily petrol prices as the dependent variable and the international oil price, ZMW/US$ exchange rate, excise duty, VAT, and inflation as the independent variables, covering a period from January 2011 to September 2025. The performance evaluation revealed that Ridge Linear Regression consistently outperformed the other models, scoring a MAE of 0.0510, RMSE of 0.2356, R² of 0.9927, a DM statistic of -2.6793, and p-value of 0.0074. Explainable AI (XAI) techniques, including SHAP values, feature importance, and partial dependence plots, were integrated to enhance interpretability. The XAI results indicate that excise duty, VAT, the ZMW/US$ exchange rate, and the international oil price are dominant drivers of petrol price movements, while inflation plays a limited direct role.