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Artificial Intelligence-Based Prediction of Crude Oil Prices Using Multiple Features under the Effect of Russia–Ukraine War and COVID-19 Pandemic

Author

Listed:
  • Hadi Jahanshahi

    (Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada)

  • Süleyman Uzun

    (Computer Engineering Department, Technology Faculty, Sakarya University of Applied Sciences, Sakarya 54050, Turkey)

  • Sezgin Kaçar

    (Electrical and Electronics Engineering Department, Technology Faculty, Sakarya University of Applied Sciences, Sakarya 54050, Turkey)

  • Qijia Yao

    (School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Madini O. Alassafi

    (Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

The effect of the COVID-19 pandemic on crude oil prices just faded; at this moment, the Russia–Ukraine war brought a new crisis. In this paper, a new application is developed that predicts the change in crude oil prices by incorporating these two global effects. Unlike most existing studies, this work uses a dataset that involves data collected over twenty-two years and contains seven different features, such as crude oil opening, closing, intraday highest value, and intraday lowest value. This work applies cross-validation to predict the crude oil prices by using machine learning algorithms (support vector machine, linear regression, and rain forest) and deep learning algorithms (long short-term memory and bidirectional long short-term memory). The results obtained by machine learning and deep learning algorithms are compared. Lastly, the high-performance estimation can be achieved in this work with the average mean absolute error value over 0.3786.

Suggested Citation

  • Hadi Jahanshahi & Süleyman Uzun & Sezgin Kaçar & Qijia Yao & Madini O. Alassafi, 2022. "Artificial Intelligence-Based Prediction of Crude Oil Prices Using Multiple Features under the Effect of Russia–Ukraine War and COVID-19 Pandemic," Mathematics, MDPI, vol. 10(22), pages 1-14, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4361-:d:978347
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    References listed on IDEAS

    as
    1. Öztunç Kaymak, Öznur & Kaymak, Yiğit, 2022. "Prediction of crude oil prices in COVID-19 outbreak using real data," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
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    3. Ravil I. Mukhamediev & Yelena Popova & Yan Kuchin & Elena Zaitseva & Almas Kalimoldayev & Adilkhan Symagulov & Vitaly Levashenko & Farida Abdoldina & Viktors Gopejenko & Kirill Yakunin & Elena Muhamed, 2022. "Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges," Mathematics, MDPI, vol. 10(15), pages 1-25, July.
    4. Petridis, Konstantinos & Tampakoudis, Ioannis & Drogalas, George & Kiosses, Nikolaos, 2022. "A Support Vector Machine model for classification of efficiency: An application to M&A," Research in International Business and Finance, Elsevier, vol. 61(C).
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    Cited by:

    1. Hind Aldabagh & Xianrong Zheng & Mohammad Najand & Ravi Mukkamala, 2024. "Forecasting Crude Oil Price Using Multiple Factors," JRFM, MDPI, vol. 17(9), pages 1-15, September.
    2. Zhou, Wei-Xing & Dai, Yun-Shi & Duong, Kiet Tuan & Dai, Peng-Fei, 2024. "The impact of the Russia-Ukraine conflict on the extreme risk spillovers between agricultural futures and spots," Journal of Economic Behavior & Organization, Elsevier, vol. 217(C), pages 91-111.

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