Estimation of global natural gas spot prices using big data and symbolic regression

Ljubiša Stajić, Renáta Praksová, Dejan Brkić and Pavel Praks

Resources Policy, 2024, vol. 95, issue C

Abstract: This article provides an estimation of future natural gas spot prices on the global international market based on symbolic regression where the sensitivity analysis is performed to identify the most important input parameters. Numerical data sets, comprising various parameters, some of which demonstrate stronger correlations with the global spot price of natural gas, are utilised in this context. PySR (Python Symbolic Regression), a free and open-source software for symbolic regression written in Python, and Julia is used for the presented analysis. Based on the accuracy of the prediction and after sensitivity analysis performed in SALib software, some of the parameters are discovered to be more influencing on natural gas prices compared with others, making this approach suitable for further deeper energy analysis. The analysis shows that in general, global prices of natural gas are influenced mostly by the price of crude oil. The article also presents an overview of methods for predicting natural gas prices with a complementary contribution (interpretable models provided by symbolic regression and sensitivity analysis) tested on the real gas price time-series dataset.

Keywords: Natural gas; Spot prices; Symbolic regression; Market price forecasting; Supercomputing; Sensitivity analysis (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0301420724005117
Full text for ScienceDirect subscribers only

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:eee:jrpoli:v:95:y:2024:i:c:s0301420724005117

DOI: 10.1016/j.resourpol.2024.105144

Access Statistics for this article

Resources Policy is currently edited by R. G. Eggert

More articles in Resources Policy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().