Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods

Moting Su, Zongyi Zhang, Ye Zhu, Donglan Zha and Wenying Wen
Additional contact information
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, Victoria 3125, Australia
Donglan Zha: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Wenying Wen: School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China

Energies, 2019, vol. 12, issue 9, 1-17

Abstract: Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR.

Keywords: natural gas price; natural gas price forecasting; prediction model; machine learning methods (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 (9)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/9/1680/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/9/1680/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:9:p:1680-:d:228064

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().