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

Renchu Guan, Aoqing Wang, Yanchun Liang, Jiasheng Fu and Xiaosong Han
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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
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

Energies, 2022, vol. 15, issue 10, 1-14

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.

Keywords: natural gas; machine learning; price trend prediction (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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