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Crude Oil Trend Prediction During COVID-19: Machine Learning with Randomized Search and Bayesian Optimization

Author

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  • Seyyed Soroosh Firoozabadi

    (Tarbiat Modares University, Iran)

  • Mehdi Ansari

    (Oklahoma State University, United States)

  • Farhad Vasheghanifarahani

    (University of South Florida, United States)

Abstract

This study delves into an innovative research framework aimed at enhancing the precision of crude oil return rate predictions. The study, which holds significant implications for financial institutions, investors, central banks, and corporations operating in volatile markets, rigorously evaluates the performance of three advanced machine learning models—LSTM, XGBoost, and SVM. Leveraging optimization and cross-validation techniques, the research particularly focuses on refining forecasting accuracy amidst the challenges posed by the COVID-19 epidemic. This study explores randomized search and Bayesian optimization, providing a comprehensive understanding of their application in the context of improving model performance and decision-making in the dynamic crude oil market. The findings indicate the accuracy of models with different evaluation metrics and reveal that the SVM demonstrates superior accuracy in regression analysis during the pandemic.

Suggested Citation

  • Seyyed Soroosh Firoozabadi & Mehdi Ansari & Farhad Vasheghanifarahani, 2024. "Crude Oil Trend Prediction During COVID-19: Machine Learning with Randomized Search and Bayesian Optimization," European Journal of Business and Management Research, European Open Science, vol. 9(3), pages 6-13, May.
    • Handle: RePEc:epw:ejbmr0:v:9:y:2024:i:3:id:52221
    DOI: 10.24018/ejbmr.2024.9.3.2221
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