{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:06Z","timestamp":1790035206769,"version":"4.0.1"},"reference-count":74,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Social Science Fund of China","award":["20220101039JC"],"award-info":[{"award-number":["20220101039JC"]}]},{"name":"the Science and Technology Development Project of Jilin Province","award":["20220101039JC"],"award-info":[{"award-number":["20220101039JC"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>The natural gas supply crisis triggered by the Russia\u2013Ukraine conflict has laid bare the energy market\u2019s 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\u20134 days in advance, optimizing import reserve strategies to avoid supply disruptions; energy traders can use this to design robust futures arbitrage portfolios. 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