IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i10p3573-d814831.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/10/3573/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/10/3573/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Theodosios Anastasios Perifanis, 2022. "The Macroeconomic Results of Diligent Resource Revenues Management: The Norwegian Case," Energies, MDPI, vol. 15(4), pages 1-14, February.
    2. Krzysztof Drachal, 2022. "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression," Energies, MDPI, vol. 16(1), pages 1-29, December.
    3. Garrod Brian & Almeida António & Machado Luiz, 2023. "Modelling of nonlinear asymmetric effects of changes in tourism on economic growth in an autonomous small-island economy," European Journal of Tourism, Hospitality and Recreation, Sciendo, vol. 13(2), pages 154-172, December.
    4. Prince Waqas Khan & Yung-Cheol Byun & Sang-Joon Lee & Dong-Ho Kang & Jin-Young Kang & Hae-Su Park, 2020. "Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources," Energies, MDPI, vol. 13(18), pages 1-16, September.
    5. Aharon, David Y. & Azman Aziz, Mukhriz Izraf & Kallir, Ido, 2023. "Oil price shocks and inflation: A cross-national examination in the ASEAN5+3 countries," Resources Policy, Elsevier, vol. 82(C).
    6. Periklis Gogas & Theophilos Papadimitriou, 2023. "Machine Learning in Renewable Energy," Energies, MDPI, vol. 16(5), pages 1-3, February.
    7. Shangfeng Han & Baosheng Zhang & Xiaoyang Sun & Song Han & Mikael Höök, 2017. "China’s Energy Transition in the Power and Transport Sectors from a Substitution Perspective," Energies, MDPI, vol. 10(5), pages 1-25, April.
    8. Zhuo Chen & Bo Yan & Hanwen Kang, 2022. "Dynamic correlation between crude oil and agricultural futures markets," Review of Development Economics, Wiley Blackwell, vol. 26(3), pages 1798-1849, August.
    9. Javier López Gómez & Ana Ogando Martínez & Francisco Troncoso Pastoriza & Lara Febrero Garrido & Enrique Granada Álvarez & José Antonio Orosa García, 2020. "Photovoltaic Power Prediction Using Artificial Neural Networks and Numerical Weather Data," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    10. Yanling Li & Mengxin Wang & Gaoke Liao & Junxia Wang, 2022. "Spatial Spillover Effect and Threshold Effect of Digital Financial Inclusion on Farmers’ Income Growth—Based on Provincial Data of China," Sustainability, MDPI, vol. 14(3), pages 1-16, February.
    11. Yuriy Bilan & Serhiy Kozmenko & Alex Plastun, 2022. "Price Forecasting in Energy Market," Energies, MDPI, vol. 15(24), pages 1-6, December.
    12. Kumeka, Terver Theophilus & Uzoma-Nwosu, Damian Chidozie & David-Wayas, Maria Onyinye, 2022. "The effects of COVID-19 on the interrelationship among oil prices, stock prices and exchange rates in selected oil exporting economies," Resources Policy, Elsevier, vol. 77(C).
    13. Bei Zhang & Xiaoqing Ai & Xingming Fang & Shi Chen, 2022. "The Transmission Mechanisms and Impacts of Oil Price Fluctuations: Evidence from DSGE Model," Energies, MDPI, vol. 15(16), pages 1-20, August.
    14. Antonio José Orozco-Gallo & Pavel Vidal-Alejandro & Johana Sanabria-Domínguez & Jaime Andrés Collazos-Rodríguez, 2021. "Indicador coincidente de actividad económica en la recesión pandémica: el caso del Caribe colombiano," Documentos de trabajo sobre Economía Regional y Urbana 298, Banco de la Republica de Colombia.
    15. Yadong Pei & Chiou-Jye Huang & Yamin Shen & Mingyue Wang, 2023. "A Novel Model for Spot Price Forecast of Natural Gas Based on Temporal Convolutional Network," Energies, MDPI, vol. 16(5), pages 1-15, February.
    16. Oktay Ozkan & Salah Abosedra & Arshian Sharif & Andrew Adewale Alola, 2024. "Dynamic volatility among fossil energy, clean energy and major assets: evidence from the novel DCC-GARCH," Economic Change and Restructuring, Springer, vol. 57(3), pages 1-19, June.
    17. Ahmad Al Humssi & Maria Petrovskaya & Milana Abueva, 2022. "Modelling the Impact of World Oil Prices and the Mining and Quarrying Sector on the United Arab Emirates’ GDP," Mathematics, MDPI, vol. 11(1), pages 1-22, December.
    18. An, Jaehyung & Mikhaylov, Alexey & Chang, Tsangyao, 2024. "Relationship between the popularity of a platform and the price of NFT assets," Finance Research Letters, Elsevier, vol. 61(C).
    19. Adnan Khattak & Rasool Bukhsh & Sheraz Aslam & Ayman Yafoz & Omar Alghushairy & Raed Alsini, 2022. "A Hybrid Deep Learning-Based Model for Detection of Electricity Losses Using Big Data in Power Systems," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    20. Gerson Javier Pérez-Valbuena & Diana Ricciulli-Marín & Jaime Bonet-Morón & Paula Barrios, 2021. "Reglas fiscales subnacionales en Colombia: desde su concepción hasta los resultados frente al COVID-19," Documentos de Trabajo Sobre Economía Regional y Urbana 19126, Banco de la República, Economía Regional.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3573-:d:814831. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.