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Enhancing Stock Price Trend Prediction via a Time-Sensitive Data Augmentation Method
Joint Authors
Teng, Xiao
Wang, Tuo
Zhang, Xiang
Lan, Long
Luo, Zhigang
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-17
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Stock trend prediction refers to predicting future price trend of stocks for seeking profit maximum of stock investment.
Although it has aroused broad attention in stock markets, it is still a tough task not only because the stock markets are complex and easily volatile but also because real short-term stock data is so limited that existing stock prediction models could be far from perfect, especially for deep neural networks.
As a kind of time-series data, the underlying patterns of stock data are easily influenced by any tiny noises.
Thus, how to augment limited stock price data is an open problem in stock trend prediction, since most data augmentation schemes adopted in image processing cannot be brutally used here.
To this end, we devise a simple yet effective time-sensitive data augmentation method for stock trend prediction.
To be specific, we augment data by corrupting high-frequency patterns of original stock price data as well as preserving low-frequency ones in the frame of wavelet transformation.
The proposed method is motivated by the fact that low-frequency patterns without noisy corruptions do not hurt the true patterns of stock price data.
Besides, a transformation technique is proposed to recognize the importance of the patterns at varied time points, that is, the information is time-sensitive.
A series of experiments carried out on a real stock price dataset including 50 corporation stocks verify the efficacy of our data augmentation method.
American Psychological Association (APA)
Teng, Xiao& Wang, Tuo& Zhang, Xiang& Lan, Long& Luo, Zhigang. 2020. Enhancing Stock Price Trend Prediction via a Time-Sensitive Data Augmentation Method. Complexity،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1143327
Modern Language Association (MLA)
Teng, Xiao…[et al.]. Enhancing Stock Price Trend Prediction via a Time-Sensitive Data Augmentation Method. Complexity No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1143327
American Medical Association (AMA)
Teng, Xiao& Wang, Tuo& Zhang, Xiang& Lan, Long& Luo, Zhigang. Enhancing Stock Price Trend Prediction via a Time-Sensitive Data Augmentation Method. Complexity. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1143327
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1143327