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Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods

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  • Moting Su

    (School of Economics and Business Administration, Chongqing University, Chongqing 400030, China)

  • Zongyi Zhang

    (School of Economics and Business Administration, Chongqing University, Chongqing 400030, China)

  • Ye Zhu

    (School of Information Technology, Deakin University, Victoria 3125, Australia)

  • Donglan Zha

    (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Wenying Wen

    (School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China)

Abstract

Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR.

Suggested Citation

  • Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha & Wenying Wen, 2019. "Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods," Energies, MDPI, vol. 12(9), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1680-:d:228064
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