Applied attention-based LSTM neural networks in stock prediction

被引：0

作者：

Cheng, Li-Chen ^{[1
]}

Huang, Yu-Hsiang ^{[1
]}

Wu, Mu-En ^{[2
]}

机构：

[1] Soochow Univ, Dept Comp Sci & Informat Management, Taipei, Taiwan

[2] Natl Taipei Univ Technol, Dept Informat & Finance Management, Taipei, Taiwan

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2018年

关键词：

deep learning; stock prediction; attention mechanism;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. This paper proposes an attention-based long short-term memory model to predict stock price movement and make trading strategies

引用

页码：4716 / 4718

页数：3

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