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Oil Commodity Movement Estimation: Analysis with Gaussian Process and Data Science

Author

Listed:
  • Mulue Gebreslasie

    (Department of Mathematics, North Dakota State University, Fargo, ND 58108, USA)

  • Indranil SenGupta

    (Department of Mathematics and Statistics, Hunter College, City University of New York (CUNY), New York City, NY 10065, USA
    Environmental Sciences Initiative, Advanced Science Research Center–CUNY, New York City, NY 10031, USA)

Abstract

In this study, Gaussian process (GP) regression is used to normalize observed commodity data and produce predictions at densely interpolated time intervals. The methodology is applied to an empirical oil price dataset. A Gaussian kernel with data-dependent initialization is used to calculate prediction means and confidence intervals. This approach generates synthetic data points from the denoised dataset to improve prediction accuracy. From this augmented larger dataset, a procedure is developed for estimating an upcoming crash-like behavior of the commodity price. Finally, multiple data-science-driven algorithms are used to demonstrate how data densification using GP regression improves the detection of forthcoming large fluctuations in a particular commodity dataset.

Suggested Citation

  • Mulue Gebreslasie & Indranil SenGupta, 2025. "Oil Commodity Movement Estimation: Analysis with Gaussian Process and Data Science," Commodities, MDPI, vol. 4(2), pages 1-17, June.
    • Handle: RePEc:gam:jcommo:v:4:y:2025:i:2:p:9-:d:1677378
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    References listed on IDEAS

    as
    1. Humayra Shoshi & Erik Hanson & William Nganje & Indranil SenGupta, 2021. "Stochastic Analysis and Neural Network-Based Yield Prediction with Precision Agriculture," JRFM, MDPI, vol. 14(9), pages 1-17, August.
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