Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 235-239.doi: 10.11896/jsjkx.201000056

• Big Data & Data Science • Previous Articles     Next Articles

Prediction Method of International Natural Gas Price Trends Based on News

PEI Ying1, LI Tian-xiang2,3, WANG Ao-qing4, FU Jia-sheng5, HAN Xiao-song4   

  1. 1 College of Information Engineering,Changchun University of Finance and Economics,Changchun 130122,China
    2 University of Chinese Academy of Sciences,Beijing 100049,China
    3 Xinjiang Technical Institute of Physics and Chemistry,Chinese Academy of Sciences,Urumqi 830011,China
    4 Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry,College of Computer Science and Technology,Jilin University,Changchun 130012,China
    5 CNPC Engineering Technology R&D Company Limited,Beijing 102206,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:PEI Ying,born in 1990,Ph.D student,assistant professor.Her main research interests include financial big data analysis and machine learning.
    HAN Xiao-song,born in 1984,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include machine lear-ning and optimization algorithm.
  • Supported by:
    National Natural Science Foundation of China(61972174),Science Technology Development Project of Jilin Province(20190302107GX),Special Research and Development of Industrial Technology of Jilin Province(2019C053-7),Guangdong Key Project for Applied Fundamental Research(2018KZDXM076) and Guangdong Premier Key-Discipline Enhancement Scheme(2016GDYSZDXK036).

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Abstract: As a new type of clean and important energy,natural gas is one of the bulk commodities of futures trading.As an important component of the national economy and international transactions,it has important economic significance.However,due to the influence of economic,political,natural and even human factors on the price of natural gas,it is very difficult to predict the price accurately.Therefore,a news-based prediction model of natural gas price trends is proposed in this paper.In this model,text embedding and sentiment analysis are conducted on natural gas-related news.The Granger causality test is employed to prove the causality between the price of natural gas and the emotional tendency of relevant news.The news sentiment is multiplied as the weight of the news vector,and the weighted vectors are the input of CNN and LSTM fused model.CNN is used to extract news features,LSTM is used to capture time series information of news and natural gas price trends.Finally,the network achieves an accuracy as 62%.The accuracy is still better than most traditional machine learning algorithms.

Key words: Causality test, Deep learning, Gas price, Nature language process, News

CLC Number: 

  • TP391.1
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