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. Attached Files Name Description MIMEType Size Downloads Title Data-driven natural gas spot price forecasting with least squares regression boosting algorithm Author(s) Su, Moting Zhang, Zongyi 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 Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30121168 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Link to full-text (open access)   Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Tue, 30 Apr 2019, 15:26:24 EST Tue, 30 Apr 2019, 16:01:55 EST Wed, 01 May 2019, 06:03:06 EST Thu, 02 May 2019, 14:35:34 EST Thu, 02 May 2019, 14:37:59 EST Sat, 08 Jun 2019, 02:30:37 EST Fri, 15 Nov 2019, 07:36:14 EST Filtered Full Citation counts:   Cited 4 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 221 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Tue, 30 Apr 2019, 15:26:24 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.