An Ensembled Model for the Prediction of Oil Price

Abstract

This study develops an ensemble framework combining phase space reconstruction and support vector machines to predict oil prices, crucial for economic regulation in energy markets. We analyzed five representative crude oils from spot and futures markets. Our method provides reliable 18-day predictions, demonstrating robustness against non-stationary, noisy data. Compared to traditional models, it shows superior performance, enhancing market stability and surveillance. This research offers a valuable predictive tool for policymakers and market participants, supporting informed decision-making in economic governance.