计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 235-239.doi: 10.11896/jsjkx.201000056
裴莹1, 李天祥2,3, 王鏖清4, 付加胜5, 韩霄松4
PEI Ying1, LI Tian-xiang2,3, WANG Ao-qing4, FU Jia-sheng5, HAN Xiao-song4
摘要： 天然气作为新型清洁能源,不仅有着重要的能源意义,作为期货交易的大宗商品之一,也有着重要的经济意义,是国家经济和国际贸易的重要组成。但是由于天然气价格受经济因素、政治因素、自然因素甚至人为因素等多种因素的影响,准确预测其价格十分困难。因此,文中设计了一种基于新闻的天然气价格趋势预测方法,该方法首先利用爬虫获取大量天然气相关新闻,并针对新闻进行嵌入表示和情感分析,运用格兰杰因果检验方法证明了天然气价格与相关新闻的情感倾向具有因果关系,并将新闻情感作为新闻向量的权值,将其相乘作为模型输入,然后构建了一个CNN-LSTM融合模型,CNN用于提取新闻特征,LSTM用于捕捉新闻和天然气价格时间序列信息,从而得到了62%的准确率,优于绝大多数机器学习算法。
中图分类号:
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