[1]闫洪举.金融时间序列数据预测:文献回顾与展望[J].金融教育研究,2021,(03):33-41.
　YAN Hongju.Financial Time Series Prediction:A Literature Review and Prospect[J].,2021,(03):33-41.
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金融时间序列数据预测:文献回顾与展望()
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《金融教育研究》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2021年03期
页码:
33-41
栏目:
金融论坛
出版日期:
2021-05-25

文章信息/Info

Title:
Financial Time Series Prediction:A Literature Review and Prospect
文章编号:
2095-0098(2021)03-0033-09
作者:
闫洪举1; 2
1.中国农业银行 博士后科研工作站,北京 100005; 2.北京大学 经济学院,北京 100871
Author(s):
YAN Hongju1; 2
1.Postdoctoral Research Station of Agricultural Bank of China,Beijing 100005,China; 2.School of Economics,Peking University,Beijing 100871,China
关键词:
时间序列数据;  计量经济学;  机器学习;  深度学习
Keywords:
Time series data;  Econometric model;  Machine learning;  Artificial intelligence
分类号:
F832.29
文献标志码:
A
摘要:
金融时间序列数据具有非线性、非平稳、高噪声等复杂特征,且随着移动互联网、人工智能的快速发展,海量结构化与非结构化数据不断产生,数据间的关联模式日益复杂。在此背景之下,构建科学合理的金融时间序列数据预测模型,充分挖掘金融时间序列数据隐含的重要信息至关重要。为此,在梳理金融时间序列数据预测的计量经济学方法、机器学习算法的基础上,着重分析深度学习应用于金融时间序列数据预测的理论基础与实证应用的相关文献,以期为大数据与人工智能背景下的金融时间序列数据预测以及多学科交叉融合研究提供相关借鉴。
Abstract:
Financial time series data has complex characteristics such as non-linear,non-stationary and high noise.With the rapid development of mobile internet and artificial intelligence,massive structured and unstructured data are constantly generated,and the relation among them is increasingly complex.So,it is very important to build a scientific and reasonable financial time series data prediction model and fully mine the important information hidden in the financial time series data.Compared with econometric model and machine learning,deep learning algorithm,which has achieved great success in image recognition,self-driving,natural language and many other fields of artificial intelligence,is more suitable for financial time series data prediction under the background of big data and artificial intelligence.

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备注/Memo

备注/Memo:
收稿日期:2020-04-08
作者简介:闫洪举(1987-),山东乐陵人,博士,研究方向为金融经济学。
更新日期/Last Update: 2021-06-10