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

1
School of Economics and Business Administration, Chongqing University, Chongqing 400030, China
2
School of Information Technology, Deakin University, Melbourne, VIC 3125, Australia
3
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
*
Authors to whom correspondence should be addressed.
Energies 2019, 12(6), 1094; https://doi.org/10.3390/en12061094
Received: 17 February 2019 / Revised: 12 March 2019 / Accepted: 18 March 2019 / Published: 21 March 2019
(This article belongs to the Special Issue Climate Changes and Energy Markets)
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PDF [746 KB, uploaded 28 March 2019]
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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. View Full-Text
Keywords: natural gas spot prices; henry hub; least square regression boosting (LSBoost) natural gas spot prices; henry hub; least square regression boosting (LSBoost)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Su, M.; Zhang, Z.; Zhu, Y.; Zha, D. Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm. Energies 2019, 12, 1094.

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