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Forecasting Crude Oil Prices: A Machine Learning Perspective

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Computing, Communication and Learning (CoCoLe 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1892))

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Abstract

The crude oil price (COP) has substantial implications on world economy, as it impacts industries ranging from transportation to manufacturing. Given the volatile nature of COP, accurate forecasting is very much crucial for businesses and policymakers alike. Forecasting crude oil prices is a challenging task for the complex and volatile nature of the global oil market. As a result, estimating the price of crude oil has been a challenging and crucial component of forecasting research. In this study, we employ fourteen machine learning (ML) models for predicting the weekly and daily crude oil price. To evaluate the effectiveness of ML models, four performance measure metrics are utilized, including “mean absolute scaled error (MASE), symmetric mean absolute percentage error (SMAPE), root mean square error (RMSE), and mean absolute error (MAE)”. Detailed statistical analyses of data obtained using the Wilcoxon Signed-Rank test demonstrate that the linear support vector regression (SVR) model for weekly COP data, and linear regression for daily COP data are statistically more effective in predicting COPs than other models considered. The linear regression model acquires the statistically best rank across three accuracy metrics (SMAPE, MAE, MASE) and Gradient Boosting acquires the best rank based on RMSE accuracy metrics considering both weekly and daily COP data according to the Friedman and Nemenyi hypothesis test.

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Acknowledgements

This research paper is catalyzed and supported by the Science and Engineering Research Board (SERB), DST, Government of India with Grant No. CRG/2021/006122.

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Authors and Affiliations

  1. Department of Computer Science Engineering and Applications, Sambalpur University Institute of Information Technology, Jyoti Vihar, Burla, Odisha, 768019, India

    Sourav Kumar Purohit

  2. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India

    Sibarama Panigrahi

Authors
  1. Sourav Kumar Purohit
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  2. Sibarama Panigrahi
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Corresponding author

Correspondence to Sibarama Panigrahi .

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Editors and Affiliations

  1. National Institute of Technology, Warangal, India

    Sanjaya Kumar Panda

  2. National Institute of Technology, Warangal, India

    Rashmi Ranjan Rout

  3. National Institute of Technology, Warangal, India

    Manjubala Bisi

  4. National Institute of Technology, Warangal, India

    Ravi Chandra Sadam

  5. Providence University, Taichung City, Taiwan

    Kuan-Ching Li

  6. University of Milan, Milan, Italy

    Vincenzo Piuri

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Purohit, S.K., Panigrahi, S. (2024). Forecasting Crude Oil Prices: A Machine Learning Perspective. In: Panda, S.K., Rout, R.R., Bisi, M., Sadam, R.C., Li, KC., Piuri, V. (eds) Computing, Communication and Learning. CoCoLe 2023. Communications in Computer and Information Science, vol 1892. Springer, Cham. https://doi.org/10.1007/978-3-031-56998-2_2

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  • DOI: https://doi.org/10.1007/978-3-031-56998-2_2

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-56998-2

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