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Forecasting Natural Gas Spot Prices with Machine Learning

Author

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  • Dimitrios Mouchtaris

    (Faculty of Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Emmanouil Sofianos

    (Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece)

  • Periklis Gogas

    (Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece)

  • Theophilos Papadimitriou

    (Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece)

Abstract

The ability to accurately forecast the spot price of natural gas benefits stakeholders and is a valuable tool for all market participants in the competitive gas market. In this paper, we attempt to forecast the natural gas spot price 1, 3, 5, and 10 days ahead using machine learning methods: support vector machines (SVM), regression trees, linear regression, Gaussian process regression (GPR), and ensemble of trees. These models are trained with a set of 21 explanatory variables in a 5-fold cross-validation scheme with 90% of the dataset used for training and the remaining 10% used for testing the out-of-sample generalization ability. The results show that these machine learning methods all have different forecasting accuracy for every time frame when it comes to forecasting natural gas spot prices. However, the bagged trees (belonging to the ensemble of trees method) and the linear SVM models have superior forecasting performance compared to the rest of the models.

Suggested Citation

  • Dimitrios Mouchtaris & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2021. "Forecasting Natural Gas Spot Prices with Machine Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5782-:d:634984
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    References listed on IDEAS

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    Cited by:

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    2. Sun-Feel Yang & So-Won Choi & Eul-Bum Lee, 2023. "A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices," Energies, MDPI, vol. 16(11), pages 1-39, May.
    3. Yadong Pei & Chiou-Jye Huang & Yamin Shen & Mingyue Wang, 2023. "A Novel Model for Spot Price Forecast of Natural Gas Based on Temporal Convolutional Network," Energies, MDPI, vol. 16(5), pages 1-15, February.
    4. Stajić, Ljubiša & Praksová, Renáta & Brkić, Dejan & Praks, Pavel, 2024. "Estimation of global natural gas spot prices using big data and symbolic regression," Resources Policy, Elsevier, vol. 95(C).
    5. Tiwari, Aviral Kumar & Sharma, Gagan Deep & Rao, Amar & Hossain, Mohammad Razib & Dev, Dhairya, 2024. "Unraveling the crystal ball: Machine learning models for crude oil and natural gas volatility forecasting," Energy Economics, Elsevier, vol. 134(C).
    6. Periklis Gogas & Theophilos Papadimitriou, 2022. "Emerging Trends in Energy Economics," Energies, MDPI, vol. 15(14), pages 1-2, July.

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