Machine Learning WTI Crude Oil Price Predictions

Bingzi Jin and Xiaojie Xu
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Bingzi Jin: Advanced Micro Devices (China) Co., Ltd., Shanghai, China
Xiaojie Xu: ��North Carolina State University, Raleigh, NC 27695, USA

Journal of International Commerce, Economics and Policy (JICEP), 2025, vol. 16, issue 01, 1-18

Abstract: Energy commodity price forecasts have always been quite important to a lot of market participants. To tackle the problem, our analysis looks at West Texas Intermediate (WTI) crude oil prices on a daily basis. The sample under inquiry covers 10 years, from April 4, 2014 to April 3, 2024, and the price series under analysis has major financial implications. Here, Gaussian process regression methods are developed using Bayesian optimization techniques and cross-validation processes, and the resulting strategies are utilized to provide price projections. The relative root mean square error of 2.2743% indicates that our empirical prediction approach produces reasonably accurate price estimates for the out-of-sample assessment period of April 19, 2022–April 3, 2024. Models of price predictions give investors and governments the information they need to make wise choices about the crude oil market.

Keywords: WTI crude oil price; time-series forecast; Gaussian process regression; Bayesian optimization; cross-validation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S1793993325500048

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