Data-driven natural gas spot price forecasting with least squares regression boosting algorithm

Su, Moting, Zhang, Zongyi, Zhu, Ye and Zha, Donglan 2019, Data-driven natural gas spot price forecasting with least squares regression boosting algorithm, Energies, vol. 12, no. 6, pp. 1094-1094, doi: 10.3390/en12061094.

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Title Data-driven natural gas spot price forecasting with least squares regression boosting algorithm
Author(s) Su, Moting
Zhang, Zongyi
Zhu, YeORCID iD for Zhu, Ye orcid.org/0000-0003-4776-4932
Zha, Donglan
Journal name Energies
Volume number 12
Issue number 6
Start page 1094
End page 1094
Total pages 13
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2019
ISSN 1996-1073
Summary © 2019 by the authors. 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.
Language eng
DOI 10.3390/en12061094
Field of Research 09 Engineering
02 Physical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2019, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30121168

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