A voting regressor ensemble model for crude oil price prediction

Abstract

Accurate forecasting of crude oil prices is crucial for informed decision-making in global energy markets, financial planning, and policy formulation. This study examines the effectiveness of regression models in forecasting crude oil prices using historical data augmented with lagged features to capture temporal dynamics. Four models were evaluated, including gradient boosting, random forest, support vector regression (SVR), and a voting regressor ensemble that integrates predictions from both random forest and gradient boosting. The dataset was transformed using a seven-day lag structure, and model performance was evaluated using root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²). Results indicate that the voting regressor ensemble outperformed individual models, achieving the best performance metrics (RMSE = 3.5978, MAE = 1.9816, R² = 0.9363). Random forest and gradient boosting models also demonstrated robust performance (R² > 0.93). In contrast, SVR demonstrated relatively lower performance with an R² of 0.5614 and significantly higher error metrics. The results confirm that the ensemble voting regressor provides superior prediction accuracy for crude oil price forecasting. This finding has important implications for financial analysts, policymakers, and energy sector stakeholders aiming to improve decision-making based on reliable crude oil price forecasts.