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

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  • Stajić, Ljubiša
  • Praksová, Renáta
  • Brkić, Dejan
  • Praks, Pavel

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.

Suggested Citation

  • Stajić, Ljubiša & Praksová, Renáta & Brkić, Dejan & Praks, Pavel, 2024. "Estimation of global natural gas spot prices using big data and symbolic regression," Resources Policy, Elsevier, vol. 95(C).
    • Handle: RePEc:eee:jrpoli:v:95:y:2024:i:c:s0301420724005117
    DOI: 10.1016/j.resourpol.2024.105144
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