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International Natural Gas Price Trends Prediction with Historical Prices and Related News

Author

Listed:
  • Renchu Guan

    (Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of National Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Aoqing Wang

    (Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of National Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Yanchun Liang

    (Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of National Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
    Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Science and Technology, Zhuhai 519041, China)

  • Jiasheng Fu

    (CNPC Engineering Technology R&D Company Limited, Beijing 102206, China)

  • Xiaosong Han

    (Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of National Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China)

Abstract

Under the idea of low carbon economy, natural gas has drawn widely attention all over the world and becomes one of the fastest growing energies because of its clean, high calorific value, and environmental protection properties. However, policy and political factors, supply-demand relationship and hurricanes can cause the jump in natural gas prices volatility. To address this issue, a deep learning model based on oil and gas news is proposed to predict natural gas price trends in this paper. In this model, news text embedding is conducted by BERT-Base, Uncased on natural gas-related news. Attention model is adopted to balance the weight of the news vector. Meanwhile, corresponding natural gas price embedding is conducted by a BiLSTM module. The Attention-weighted news vectors and price embedding are the inputs of the fused network with transformer is built. BiLSTM is used to extract used price information related with news features. Transformer is employed to capture time series trend of mixed features. Finally, the network achieves an accuracy as 79%, and the performance is better than most traditional machine learning algorithms.

Suggested Citation

  • Renchu Guan & Aoqing Wang & Yanchun Liang & Jiasheng Fu & Xiaosong Han, 2022. "International Natural Gas Price Trends Prediction with Historical Prices and Related News," Energies, MDPI, vol. 15(10), pages 1-14, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3573-:d:814831
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    References listed on IDEAS

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    1. Danwei Zhang & Sergey Paltsev, 2016. "The Future Of Natural Gas In China: Effects Of Pricing Reform And Climate Policy," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 7(04), pages 1-32, November.
    2. C. Chatfield, 1978. "The Holt‐Winters Forecasting Procedure," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 27(3), pages 264-279, November.
    3. Hariprasath Manoharan & Yuvaraja Teekaraman & Irina Kirpichnikova & Ramya Kuppusamy & Srete Nikolovski & Hamid Reza Baghaee, 2020. "Smart Grid Monitoring by Wireless Sensors Using Binary Logistic Regression," Energies, MDPI, vol. 13(15), pages 1-12, August.
    4. Dimitrios Mouchtaris & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2021. "Forecasting Natural Gas Spot Prices with Machine Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.
    5. Vincenzo Candila & Denis Maximov & Alexey Mikhaylov & Nikita Moiseev & Tomonobu Senjyu & Nicole Tryndina, 2021. "On the Relationship between Oil and Exchange Rates of Oil-Exporting and Oil-Importing Countries: From the Great Recession Period to the COVID-19 Era," Energies, MDPI, vol. 14(23), pages 1-18, December.
    6. Donghua Wang & Tianhui Fang, 2022. "Forecasting Crude Oil Prices with a WT-FNN Model," Energies, MDPI, vol. 15(6), pages 1-21, March.
    7. Waldemar Tarczyński & Urszula Mentel & Grzegorz Mentel & Umer Shahzad, 2021. "The Influence of Investors’ Mood on the Stock Prices: Evidence from Energy Firms in Warsaw Stock Exchange, Poland," Energies, MDPI, vol. 14(21), pages 1-25, November.
    8. Anna Manowska & Aurelia Rybak & Artur Dylong & Joachim Pielot, 2021. "Forecasting of Natural Gas Consumption in Poland Based on ARIMA-LSTM Hybrid Model," Energies, MDPI, vol. 14(24), pages 1-14, December.
    9. Witold Orzeszko, 2021. "Nonlinear Causality between Crude Oil Prices and Exchange Rates: Evidence and Forecasting," Energies, MDPI, vol. 14(19), pages 1-16, September.
    10. Zhenghui Li & Zimei Huang & Pierre Failler, 2022. "Dynamic Correlation between Crude Oil Price and Investor Sentiment in China: Heterogeneous and Asymmetric Effect," Energies, MDPI, vol. 15(3), pages 1-22, January.
    11. Rangan Gupta & Christian Pierdzioch & Wing-Keung Wong, 2021. "A Note on Forecasting the Historical Realized Variance of Oil-Price Movements: The Role of Gold-to-Silver and Gold-to-Platinum Price Ratios," Energies, MDPI, vol. 14(20), pages 1-12, October.
    12. Marek Szturo & Bogdan Włodarczyk & Ireneusz Miciuła & Karolina Szturo, 2021. "The Essence of Relationships between the Crude Oil Market and Foreign Currencies Market Based on a Study of Key Currencies," Energies, MDPI, vol. 14(23), pages 1-17, November.
    13. Dervis Kirikkaleli & Ibrahim Darbaz, 2021. "The Causal Linkage between Energy Price and Food Price," Energies, MDPI, vol. 14(14), pages 1-13, July.
    14. Yanzhen Zhou & Junyong Wu & Zhihong Yu & Luyu Ji & Liangliang Hao, 2016. "A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier," Energies, MDPI, vol. 9(10), pages 1-20, September.
    15. Fei Wang & Yili Yu & Xinkang Wang & Hui Ren & Miadreza Shafie-Khah & João P. S. Catalão, 2018. "Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression," Energies, MDPI, vol. 11(5), pages 1-26, May.
    16. Guych Nuryyev & Tomasz Korol & Ilia Tetin, 2021. "Hold-Up Problems in International Gas Trade: A Case Study," Energies, MDPI, vol. 14(16), pages 1-16, August.
    17. Gabriel Mendonça de Paiva & Sergio Pires Pimentel & Bernardo Pinheiro Alvarenga & Enes Gonçalves Marra & Marco Mussetta & Sonia Leva, 2020. "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks," Energies, MDPI, vol. 13(11), pages 1-28, June.
    18. Radosław Puka & Bartosz Łamasz & Marek Michalski, 2021. "Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk," Energies, MDPI, vol. 14(11), pages 1-26, June.
    19. Jiaying Peng & Zhenghui Li & Benjamin M. Drakeford, 2020. "Dynamic Characteristics of Crude Oil Price Fluctuation—From the Perspective of Crude Oil Price Influence Mechanism," Energies, MDPI, vol. 13(17), pages 1-19, August.
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    1. Stephan Schlüter & Sejung Jung & Andreas von Döllen & Wonhee Lee, 2022. "An Alternative to Index-Based Gas Sourcing Using Neural Networks," Energies, MDPI, vol. 15(13), pages 1-11, June.

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