Advancing Natural Gas Price Predictions with ConcaveLSTM


  • Mohammad Diqi Universitas Respati Yogyakarta
  • Putra Wanda Universitas Respati Yogyakarta
  • Hamzah Universitas Respati Yogyakarta
  • I Wayan Ordiyasa Universitas Respati Yogyakarta
  • Azzah Fathinah Universitas Respati Yogyakarta



Financial Prediction, Machine Learning, Natural Gas, Price Forecasting, LSTM


This study investigates the application of the ConcaveLSTM model, a novel machine learning approach combining the strengths of Stacked Long Short-Term Memory (LSTM) and Bidirectional LSTM, for predicting natural gas prices. Given the inherent volatility and complexity of energy markets, accurate forecasting models are crucial for effective decision-making. The research employs a comprehensive dataset from 1997 to 2020, focusing on the daily price of natural gas in US Dollars per Million British thermal units (Btu). Through rigorous testing across various model configurations, the study identifies optimal settings for the ConcaveLSTM model that significantly improve prediction accuracy. Specifically, configurations utilizing 50 input steps with neuron counts of 100 and 300 exhibit superior performance, as evidenced by lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), alongside higher R-squared (R2) values. These findings validate the ConcaveLSTM model's potential in financial forecasting and highlight the importance of parameter tuning in enhancing model efficacy. Despite certain limitations regarding dataset scope and market variability, the results offer promising insights into developing advanced forecasting tools. Future research directions include expanding the dataset, incorporating additional market influencers, and conducting comparative analyses with other forecasting models. This study contributes to the evolving field of machine learning applications in financial market predictions, offering a foundation for further exploration and practical implementation in the energy sector.


Download data is not yet available.


N. Mohammad, W. W. Mohamad Ishak, S. I. Mustapa, and B. V. Ayodele, “Natural Gas as a Key Alternative Energy Source in Sustainable Renewable Energy Transition: A Mini Review,” Front. Energy Res., vol. 9, p. 625023, May 2021, doi: 10.3389/fenrg.2021.625023.

J. Chen, J. Yu, B. Ai, and W. Hou, “Determinants of Global Natural Gas Consumption and Import–export Flows,” Energy Economics, 2019, doi: 10.1016/j.eneco.2018.06.025.

B. Li, “Pricing Dynamics of Natural Gas Futures,” Energy Economics, 2019, doi: 10.1016/j.eneco.2018.10.024.

E. Iswandi, I. Supriyadi, and R. Ambarwati, “Understanding Gas Prices: An Overview of Regulations and Components Affecting the Indonesian Natural Gas Prices,” Iop Conference Series Earth and Environmental Science, 2021, doi: 10.1088/1755-1315/753/1/012026.

J. Chen, Z. Xiao, J. Bai, and H. Guo, “Predicting Volatility in Natural Gas Under a Cloud of Uncertainties,” Resources Policy, 2023, doi: 10.1016/j.resourpol.2023.103436.

L. Hong, H. Zhang, Y. Xie, and D. Wang, “Analysis of Factors Influencing the Henry Hub Natural Gas Price Based on Factor Analysis,” Petroleum Science, 2017, doi: 10.1007/s12182-017-0192-z.

L. Zhan and Z. Tang, “Natural Gas Price Forecasting by a New Hybrid Model Combining Quadratic Decomposition Technology and LSTM Model,” Mathematical Problems in Engineering, 2022, doi: 10.1155/2022/5488053.

S. Jiang, X.-T. Zhao, and N. Li, “Predicting the Monthly Consumption and Production of Natural Gas in the USA by Using a New Hybrid Forecasting Model Based on Two-Layer Decomposition,” Environmental Science and Pollution Research, 2023, doi: 10.1007/s11356-022-25080-4.

Y. Zheng, J. Luo, J. Chen, Z. C. Chen, and P. Shang, “Natural Gas Spot Price Prediction Research Under the Background of Russia-Ukraine Conflict - Based on FS-GA-SVR Hybrid Model,” Journal of Environmental Management, 2023, doi: 10.1016/j.jenvman.2023.118446.

Y. Pei, C.-J. Huang, Y. Shen, and M. Wang, “A Novel Model for Spot Price Forecast of Natural Gas Based on Temporal Convolutional Network,” Energies, 2023, doi: 10.3390/en16052321.

S. Bai, X. Huang, M. Luo, and J. Su, “Deep Hybrid Models for Daily Natural Gas Consumption Forecasting and Complexity Measuring,” Energy Science & Engineering, 2022, doi: 10.1002/ese3.1352.

X. Gao, Z. Gong, Q. Li, and G. Wei, “Model Selection With Decision Support Model for US Natural Gas Consumption Forecasting,” Expert Systems With Applications, 2023, doi: 10.1016/j.eswa.2023.119505.

J. Ding, Y. Zhao, and J. Jin, “Forecasting Natural Gas Consumption With Multiple Seasonal Patterns,” Applied Energy, 2023, doi: 10.1016/j.apenergy.2023.120911.

S. B. Yang and S.-W. Choi, “A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices,” Energies, 2023, doi: 10.3390/en16114271.

S. R. Mirnezami, K. Sohag, M. Jamour, F. Moridifarimani, and A. Hosseinian, “Spillovers Effect of Gas Price on Macroeconomic Indicators: A GVAR Approach,” Energy Reports, 2023, doi: 10.1016/j.egyr.2023.05.222.

M. Tong, F. Qin, and J. Dong, “Natural Gas Consumption Forecasting Using an Optimized Grey Bernoulli Model: The Case of the World’s Top Three Natural Gas Consumers,” Engineering Applications of Artificial Intelligence, 2023, doi: 10.1016/j.engappai.2023.106005.

M. Diqi, H. Hamzah, and S. H. Mulyani, “Enhancing Weather Prediction Using Stacked Long Short- Term Memory Networks,” JATISI (Jurnal Teknik Informatika dan Sistem Informasi), vol. 10, no. 3, 2023, doi:

M. Diqi and H. Hamzah, “Improving Stock Price Prediction Accuracy With StacBi LSTM,” Jiska (Jurnal Informatika Sunan Kalijaga), 2024, doi: 10.14421/jiska.2024.9.1.10-26.




How to Cite

Diqi, M., Wanda, P., Hamzah, Ordiyasa, I. W., & Fathinah, A. (2024). Advancing Natural Gas Price Predictions with ConcaveLSTM. Techné : Jurnal Ilmiah Elektroteknika, 23(1), 163–174.