文献类型：期刊文献

中文题名：嵌入互联网舆情强度的人民币汇率预测

英文题名：RMB exchange rate forecast embedded with Internet public opinion intensity

作者：王吉祥[1];过弋[1,2,3];戚天梅[1];王志宏[1];李真[4];汤敏伟[4]

机构：[1]华东理工大学信息科学与工程学院,上海200237;[2]大数据流通与交易技术国家工程实验室,上海200237;[3]上海大数据与互联网受众工程技术研究中心,上海200072;[4]天翼电子商务有限公司风险管理部,上海200085

年份：2019

卷号：39

期号：11

起止页码：3403

中文期刊名：计算机应用

外文期刊名：journal of Computer Applications

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD_E2019_2020】；

基金：国家重点研发计划项目(2018YFC0807105);国家自然科学基金资助项目(61462073);上海市科学技术委员会科研计划项目(17DZ1101003,18511106602,18DZ2252300)~~

语种：中文

中文关键词：机器学习;文本向量化;舆情影响力;汇率预测;滑动时间窗口

外文关键词：machine learning;text embedding;public opinion impact;exchange rate forecast;temporal sliding window

摘要：针对目前人民币汇率预测研究存在的数据源单一导致难以提升预测效果的问题,提出一种嵌入互联网舆情强度的预测技术,通过融合多方面数据源进行对比分析,有效降低了人民币汇率的预测误差。首先,融合互联网外汇新闻数据和历史行情数据,并将多源文本数据转化为可计算的特征向量;其次,通过情感特征向量构建五种特征组合并对其进行对比,给出了嵌入互联网舆情强度的特征组合作为预测模型输入;最后,设计外汇舆情影响汇率预测的滑动时间窗口,建立基于机器学习的汇率预测模型。实验结果表明,嵌入互联网舆情的特征组合相对于不含舆情的特征组合在均方根误差(RMSE)和平均绝对误差(MAE)上分别提升了9.8%和16.2%;此外,长短期记忆网络(LSTM)预测模型比支持向量回归(SVR)、决策回归(DT)和深度神经网络(DNN)预测模型表现更好。
Aiming at the low prediction effect caused by single data source in the current RMB exchange rate forecast research,a forecast technology based on Internet public opinion intensity was proposed.By comparing and analyzing various data sources,the forecast error of RMB exchange rate was effectively reduced.Firstly,the Internet foreign exchange news data and historical market data were fused,and the multi-source text data were converted into the computable vectors.Secondly,five feature combinations based on sentiment feature vectors were constructed and compared,and the feature combination embedded with intensity of Internet public opinion was given as the input of forecast models.Finally,a temporal sliding window of foreign exchange public opinion data was designed,and an exchange rate forecast model based on machine learning was built.Experimental results show that feature combination embedded with Internet public opinion outperforms the feature combination without public opinion by 9.8%and 16.2%in Root Mean Squared Error(RMSE)and Mean Squared Error(MAE).At the same time,the forecast model based on Long Short-Term Memory network(LSTM)is better than that based on Support Vector Regression(SVR),Decision Tree regression(DT)and Deep Neural Network(DNN).