IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i21p14616-d965369.html
   My bibliography  Save this article

Multi-Step Crude Oil Price Prediction Based on LSTM Approach Tuned by Salp Swarm Algorithm with Disputation Operator

Author

Listed:
  • Luka Jovanovic

    (Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia)

  • Dejan Jovanovic

    (College of Academic Studies “Dositej”, Bulevar Vojvode Putnika 7, 11000 Belgrade, Serbia)

  • Nebojsa Bacanin

    (Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia)

  • Ana Jovancai Stakic

    (Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia)

  • Milos Antonijevic

    (Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia)

  • Hesham Magd

    (Business and Economics, Modern College of Business and Science, P.O. Box 100, Al-Khuwaur, Muscat 133, Oman)

  • Ravi Thirumalaisamy

    (Business and Economics, Modern College of Business and Science, P.O. Box 100, Al-Khuwaur, Muscat 133, Oman)

  • Miodrag Zivkovic

    (Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia)

Abstract

The economic model derived from the supply and demand of crude oil prices is a significant component that measures economic development and sustainability. Therefore, it is essential to mitigate crude oil price volatility risks by establishing models that will effectively predict prices. A promising approach is the application of long short-term memory artificial neural networks for time-series forecasting. However, their ability to tackle complex time series is limited. Therefore, a decomposition-forecasting approach is taken. Furthermore, machine learning model accuracy is highly dependent on hyper-parameter settings. Therefore, in this paper, a modified version of the salp swarm algorithm is tasked with determining satisfying parameters of the long short-term memory model to improve the performance and accuracy of the prediction algorithm. The proposed approach is validated on real-world West Texas Intermediate (WTI) crude oil price data throughout two types of experiments, one with the original time series and one with the decomposed series after applying variation mode decomposition. In both cases, models were adjusted to conduct one, three, and five-steps ahead predictions. According to the findings of comparative analysis with contemporary metaheuristics, it was concluded that the proposed hybrid approach is promising for crude oil price forecasting, outscoring all competitors.

Suggested Citation

  • Luka Jovanovic & Dejan Jovanovic & Nebojsa Bacanin & Ana Jovancai Stakic & Milos Antonijevic & Hesham Magd & Ravi Thirumalaisamy & Miodrag Zivkovic, 2022. "Multi-Step Crude Oil Price Prediction Based on LSTM Approach Tuned by Salp Swarm Algorithm with Disputation Operator," Sustainability, MDPI, vol. 14(21), pages 1-29, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14616-:d:965369
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/21/14616/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/21/14616/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sangyeon Kim & Myungjoo Kang, 2019. "Financial series prediction using Attention LSTM," Papers 1902.10877, arXiv.org.
    2. Zhang, Tingting & Tang, Zhenpeng & Wu, Junchuan & Du, Xiaoxu & Chen, Kaijie, 2021. "Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm," Energy, Elsevier, vol. 229(C).
    3. Nebojsa Bacanin & Ruxandra Stoean & Miodrag Zivkovic & Aleksandar Petrovic & Tarik A. Rashid & Timea Bezdan, 2021. "Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization," Mathematics, MDPI, vol. 9(21), pages 1-33, October.
    4. Dijana Jovanovic & Milos Antonijevic & Milos Stankovic & Miodrag Zivkovic & Marko Tanaskovic & Nebojsa Bacanin, 2022. "Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection," Mathematics, MDPI, vol. 10(13), pages 1-30, June.
    5. Mastroeni, Loretta & Mazzoccoli, Alessandro & Quaresima, Greta & Vellucci, Pierluigi, 2021. "Decoupling and recoupling in the crude oil price benchmarks: An investigation of similarity patterns," Energy Economics, Elsevier, vol. 94(C).
    6. Klein, Tony, 2018. "Trends and contagion in WTI and Brent crude oil spot and futures markets - The role of OPEC in the last decade," Energy Economics, Elsevier, vol. 75(C), pages 636-646.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Asit Kumar Das & Debahuti Mishra & Kaberi Das & Pradeep Kumar Mallick & Sachin Kumar & Mikhail Zymbler & Hesham El-Sayed, 2022. "Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine," Mathematics, MDPI, vol. 10(7), pages 1-33, March.
    2. Liang, Xuedong & Luo, Peng & Li, Xiaoyan & Wang, Xia & Shu, Lingli, 2023. "Crude oil price prediction using deep reinforcement learning," Resources Policy, Elsevier, vol. 81(C).
    3. Mastroeni, Loretta & Mazzoccoli, Alessandro & Quaresima, Greta & Vellucci, Pierluigi, 2021. "Decoupling and recoupling in the crude oil price benchmarks: An investigation of similarity patterns," Energy Economics, Elsevier, vol. 94(C).
    4. Ma, Richie Ruchuan & Xiong, Tao & Bao, Yukun, 2021. "The Russia-Saudi Arabia oil price war during the COVID-19 pandemic," Energy Economics, Elsevier, vol. 102(C).
    5. Qu, Fang & Chen, Yufeng & Zheng, Biao, 2021. "Is new energy driven by crude oil, high-tech sector or low-carbon notion? New evidence from high-frequency data," Energy, Elsevier, vol. 230(C).
    6. Qiang Ji & Dayong Zhang & Yuqian Zhao, 2022. "Intra-day co-movements of crude oil futures: China and the international benchmarks," Annals of Operations Research, Springer, vol. 313(1), pages 77-103, June.
    7. Michail Filippidis & George Filis & Georgios Magkonis & Panagiotis Tzouvanas, 2023. "Evaluating robust determinants of the WTI/Brent oil price differential: A dynamic model averaging analysis," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(6), pages 807-825, June.
    8. Yong-Hyuk Kim & Fabio Caraffini, 2023. "Preface to “Swarm and Evolutionary Computation—Bridging Theory and Practice”," Mathematics, MDPI, vol. 11(5), pages 1-3, March.
    9. Luo, Jiawen & Ji, Qiang & Klein, Tony & Todorova, Neda & Zhang, Dayong, 2020. "On realized volatility of crude oil futures markets: Forecasting with exogenous predictors under structural breaks," Energy Economics, Elsevier, vol. 89(C).
    10. Thi Thu Giang Nguyen & Robert Ślepaczuk, 2022. "The efficiency of various types of input layers of LSTM model in investment strategies on S&P500 index," Working Papers 2022-29, Faculty of Economic Sciences, University of Warsaw.
    11. Mehmet Sahiner & David G. McMillan & Dimos Kambouroudis, 2023. "Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(3), pages 723-762, September.
    12. Dušan S. Radivojević & Ivan M. Lazović & Nikola S. Mirkov & Uzahir R. Ramadani & Dušan P. Nikezić, 2023. "A Comparative Evaluation of Self-Attention Mechanism with ConvLSTM Model for Global Aerosol Time Series Forecasting," Mathematics, MDPI, vol. 11(7), pages 1-13, April.
    13. Zhao, Jing, 2022. "Exploring the influence of the main factors on the crude oil price volatility: An analysis based on GARCH-MIDAS model with Lasso approach," Resources Policy, Elsevier, vol. 79(C).
    14. Wu, Chunying & Wang, Jianzhou & Hao, Yan, 2022. "Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm," Resources Policy, Elsevier, vol. 77(C).
    15. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Man, Yi, 2022. "Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction," Energy, Elsevier, vol. 244(PB).
    16. Hardik A. Marfatia & Qiang Ji & Jiawen Luo, 2022. "Forecasting the volatility of agricultural commodity futures: The role of co‐volatility and oil volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 383-404, March.
    17. Klein, Tony & Todorova, Neda, 2021. "Night trading with futures in China: The case of Aluminum and Copper," Resources Policy, Elsevier, vol. 73(C).
    18. Yue Liu & Hao Dong & Pierre Failler, 2019. "The Oil Market Reactions to OPEC’s Announcements," Energies, MDPI, vol. 12(17), pages 1-15, August.
    19. Xu, Kunliang & Wang, Weiqing, 2023. "Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?," International Review of Financial Analysis, Elsevier, vol. 87(C).
    20. Lin, Boqiang & Su, Tong, 2021. "Do China's macro-financial factors determine the Shanghai crude oil futures market?," International Review of Financial Analysis, Elsevier, vol. 78(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14616-:d:965369. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.