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Price Forecasting of Crude Oil Using Hybrid Machine Learning Models

Author

Listed:
  • Jyoti Choudhary

    (Department of Mathematics, Faculty of Applied and Basic Sciences, SGT University, Gurugram 122505, India)

  • Haresh Kumar Sharma

    (Department of Operations Management and Decision Sciences, Birla Institute of Management Technology, Plot No. 5, Knowledge Park-2, Greater Noida 201306, India)

  • Pradeep Malik

    (Department of Mathematics, Faculty of Applied and Basic Sciences, SGT University, Gurugram 122505, India)

  • Saibal Majumder

    (Department of CSE (Data Science), Dr. B. C. Roy Engineering College, Durgapur 713206, India)

Abstract

Crude oil is a widely recognized, indispensable global and national economic resource. It is significantly susceptible to the boundless fluctuations attributed to various variables. Despite its capacity to sustain the global economic framework, the embedded uncertainties correlated with the crude oil markets present formidable challenges that investors must diligently navigate. In this research, we propose a hybrid machine learning model based on random forest (RF), gated recurrent unit (GRU), conventional neural network (CNN), extreme gradient boosting (XGBoost), functional partial least squares (FPLS), and stacking. This hybrid model facilitates the decision-making process related to the import and export of crude oil in India. The precision and reliability of the different machine learning models utilized in this study were validated through rigorous evaluation using various error metrics, ensuring a thorough assessment of their forecasting capabilities. The conclusive results revealed that the proposed hybrid ensemble model consistently delivered effective and robust predictions compared to the individual models.

Suggested Citation

  • Jyoti Choudhary & Haresh Kumar Sharma & Pradeep Malik & Saibal Majumder, 2025. "Price Forecasting of Crude Oil Using Hybrid Machine Learning Models," JRFM, MDPI, vol. 18(7), pages 1-25, June.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:7:p:346-:d:1684603
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    References listed on IDEAS

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