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

Moting Su, Zongyi Zhang, Ye Zhu and Donglan Zha
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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

Energies, 2019, vol. 12, issue 6, 1-13

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.

Keywords: natural gas spot prices; henry hub; least square regression boosting (LSBoost) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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