An Applied Study on Predicting Natural Gas Prices Using Mixed Models

Shu Tang, Dongphil Chun () and Xuhui Liu ()
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Shu Tang: Graduate School of Management of Technology, Pukyong National University, Busan 48547, Republic of Korea
Dongphil Chun: Graduate School of Management of Technology, Pukyong National University, Busan 48547, Republic of Korea
Xuhui Liu: Graduate School of Management of Technology, Pukyong National University, Busan 48547, Republic of Korea

Energies, 2025, vol. 18, issue 19, 1-22

Abstract: Accurate natural gas price forecasting is vital for risk management, trading strategies, and policy-making in energy markets. This study proposes and evaluates four hybrid deep learning architectures—CNN-LSTM-Attention, CNN-BiLSTM-Attention, TCN-LSTM-Attention, and TCN-BiLSTM-Attention—integrating convolutional feature extraction, sequential learning, and attention mechanisms. Using Henry Hub and NYMEX datasets, the models are trained on long historical periods and tested under multi-step horizons. The results show that all hybrid models significantly outperform the traditional moving average benchmark, achieving R 2 values above 95% for one-step-ahead forecasts and maintaining an accuracy of over 87% at longer horizons. CNN-BiLSTM-Attention performs best in short-term prediction due to its ability to capture bidirectional dependencies, while TCN-based models demonstrate greater robustness over extended horizons due to their effective modeling of long-range temporal structures. These findings confirm the advantages of deep learning hybrids in energy forecasting and emphasize the importance of horizon-sensitive evaluation. This study contributes methodological innovation and provides practical insights for market participants, with future directions including the integration of macroeconomic and climatic factors, exploration of advanced architectures such as Transformers, and probabilistic forecasting for uncertainty quantification.

Keywords: natural gas price forecasting; hybrid deep learning models; multi-step forecasting; energy markets (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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