Application of Machine Learning Techniques in Natural Gas Price Modeling. Analyses, Comparisons, and Predictions for Romania

Stelian Stancu, Alexandru Isaic-Maniu (), Constanţa-Nicoleta Bodea (), Mihai Sabin Muscalu and Denisa Elena Bălă ()
Additional contact information
Stelian Stancu: Bucharest University of Economic Studies
Alexandru Isaic-Maniu: “Costin C. Kiritescu” National Institute for Economic Research, Romanian Academy
Constanţa-Nicoleta Bodea: Bucharest University of Economic Studies
Mihai Sabin Muscalu: “Costin C. Kiritescu” National Institute for Economic Research, Romanian Academy
Denisa Elena Bălă: Bucharest University of Economic Studies

Chapter Chapter 28 in Constraints and Opportunities in Shaping the Future: New Approaches to Economics and Policy Making, 2024, pp 343-356 from Springer

Abstract: Abstract The current global energy crisis is an important topic, which emphasizes the need to study the natural gas market, with appropriate modeling methods, for a proper substantiation of the public policies. The specialized literature is generous in terms of the analyses carried out on the electricity market, but the natural gas market is not a subject fully exploited by researchers; therefore, this article represents an important contribution to knowledge in the field. This article analyses the natural gas market in Romania between November 2016 and September 2022, using data collected daily, representing the weighted average daily price of natural gas. The research is carried out with the help of advanced machine learning methods, namely, a series of basic algorithms (models), but also three categories of ensemble learning methods (bagging, boosting, and stacking). It was found that the price of natural gas in Romania can be estimated with high accuracy, using decision tree (DT) algorithms or with the help of artificial neural networks (ANNs). However, ensemble learning-based modeling proves to be the best estimation method, characterized by reduced prediction errors compared to basic models.

Keywords: Natural gas market; Price prediction; Commodity price shock; Machine learning; Ensemble modeling; Python environment (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:prbchp:978-3-031-47925-0_28

Ordering information: This item can be ordered from
http://www.springer.com/9783031479250

DOI: 10.1007/978-3-031-47925-0_28

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().