Petroleum University of Technology Petroleum Business Review 2645-4726 10 1 2026 02 01 Enhancing Crude Oil Price Forecasting through Hybrid VMD–SVR Models: Evidence from WTI Futures across Multiple Horizons 43 80 240118 10.22050/pbr.2026.556568.1420 EN Reza Maaboudi Associate Professor, Department of Economics, Faculty of Humanities, Ayatollah Boroujerdi University, Boroujerd, Iran 0000-0003-4273-5200 Mohammad Hassan Fotros Professor, Department of Economics, Faculty of Economic and Social Sciences, Bu-Ali Sina University, Hamedan, Iran Erfan Babaali M.A. Student, Department of Economics, Faculty of Humanities, Ayatollah Ozma Borujerdi University, Lorestan, Iran 0009-0001-7669-3793 Journal Article 2025 10 31 West Texas Intermediate (WTI) crude oil is a pivotal benchmark in the global energy market, exerting a decisive influence on economic expectations and national macroeconomic policies. As the primary pricing basis on the New York Mercantile Exchange and numerous energy futures contracts, WTI is subject to persistent and severe price volatility. Such fluctuations, often appearing as abrupt upward or downward shocks, profoundly affect key macroeconomic indicators. These include inflation, economic growth, trade balances, corporate profitability, production costs, and government budgets. Consequently, variations in WTI prices influence oil and gas markets, financial stability, energy security, and even international geopolitical relations. To address these challenges, this study develops a hybrid VMD+SVR framework to model and forecast WTI crude oil futures prices across short-, medium-, and long-run horizons. Empirical findings reveal that across all three horizons, the proposed hybrid model consistently achieves the lowest forecasting errors compared with alternative approaches. Moreover, the Diebold–Mariano and Wilcoxon tests statistically confirm the superior predictive performance of the hybrid VMD+SVR model. These results highlight the importance of integrating advanced adaptive signal decomposition (VMD) with powerful nonlinear learning algorithms (SVR) for accurate oil price forecasting. The proposed approach not only enhances forecasting accuracy but also provides practical insights for policymakers in managing economic risks, stabilizing budgets dependent on oil revenues, and formulating sustainable energy strategies. It opens a new avenue for developing financial forecasting models inspired by advanced signal processing. https://pbr.put.ac.ir/article_240118_61a80b230da1c1b61d57662590a34d59.pdf