Natural gas price prediction based on artificial intelligence models

Xuhui Liu, Meiqi Tang, Yu Feng, Tianhui Fang, Zhuo Wang and Mingxing Song

PLOS ONE, 2025, vol. 20, issue 12, 1-27

Abstract: The natural gas supply crisis triggered by the Russia–Ukraine conflict has laid bare the energy market’s extreme vulnerability in the face of geopolitical risk, highlighting the need for accurate multi-step gas price forecasting. However, most AI-based energy price studies have a gap: they focus on single-step prediction or homogeneous model comparisons, lacking analysis of performance degradation in multi-step dynamic frameworks. This study takes daily natural gas price data from the Henry Hub in the United States from 1997 to 2024 as the research object, constructs a multi-step prediction framework with a step size ranging from 1 to 4 days, and systematically compares the prediction performances of four artificial intelligence models: feedforward neural network, support vector machine, random forest, and long short-term memory network. The quantitative results show that, across all prediction cycles, the long short-term memory model has the lowest error rate. For example, in one-step forecasting, its Mean Absolute Percentage Error is 8.53%. Practically, the findings matter. Taking European governments facing natural gas shortages in the Russia-Ukraine conflict as an example, LSTM models can be used for multi-step prediction to forecast price fluctuations 2–4 days in advance, optimizing import reserve strategies to avoid supply disruptions; energy traders can use this to design robust futures arbitrage portfolios. In summary, the research provides a scientific basis and reference for government energy security policy-making and institutional investor trading.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0336582 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 36582&type=printable (application/pdf)

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:plo:pone00:0336582

DOI: 10.1371/journal.pone.0336582

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().