Share Price Trend Prediction Using Attention with LSTM Structure

被引：0

作者：

Jhang, Wun-Syun ^{[1
]}

Gao, Shao-En ^{[1
]}

Wang, Chuin-Mu ^{[1
]}

Hsieh, Ming-Chu ^{[1
]}

机构：

[1] Natl Chin Yi Univ Technol, Dept Comp Sci & Informat Engn, Taichung, Taiwan

来源：

2019 20TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD) | 2019年

关键词：

stock prediction; deep learning; convolutional neural network; recurrent neural network;

D O I：

10.1109/snpd.2019.8935806

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Stock market has a considerable impact in the whole financial market. Among researches on prediction, stock price movements prediction is a quite hot topic. In this paper, stock price movements were predicted by utilizing various stock information by technical means of deep learning. The architecture based on LSTM using Attention proposed in this paper was proven through experiment to be able to effectively improve prediction accuracy.

引用

页码：208 / 211

页数：4

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