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Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm

Author

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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, Melbourne, VIC 3125, Australia)

  • Donglan Zha

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

Abstract

Natural gas is often described as the cleanest fossil fuel. The consumption of natural gas is increasing rapidly. Accurate prediction of natural gas spot prices would significantly benefit energy management, economic development, and environmental conservation. In this study, the least squares regression boosting (LSBoost) algorithm was used for forecasting natural gas spot prices. LSBoost can fit regression ensembles well by minimizing the mean squared error. Henry Hub natural gas spot prices were investigated, and a wide range of time series from January 2001 to December 2017 was selected. The LSBoost method is adopted to analyze data series at daily, weekly and monthly. An empirical study verified that the proposed prediction model has a high degree of fitting. Compared with some existing approaches such as linear regression, linear support vector machine (SVM), quadratic SVM, and cubic SVM, the proposed LSBoost-based model showed better performance such as a higher R-square and lower mean absolute error, mean square error, and root-mean-square error.

Suggested Citation

  • Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha, 2019. "Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm," Energies, MDPI, vol. 12(6), pages 1-13, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1094-:d:215937
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    5. 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.
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