Paper Info
Title
Multi-Factor Based Stock Price Prediction Using Hybrid Neural Networks With Attention Mechanism
Abstract
The prediction of time series data, such as stock prices, is difficult since there exist many factors that affect the prediction model. Also, the influence of different factors on a stock price may be linear or nonlinear. The generation of good models for stock prices challenge the researchers in recent years. Long Short-Term Memory (LSTM) is a variation of Recurrent Neural Network (RNN), which can capture temporal sequence and have gained great success on time series prediction. Also, Convolutional Neural Network (CNN) is superior for extracting features from multi-dimensional sequences. In this paper, we propose a CNN-LSTM hybrid neural network with multiple factors to predict stock prices. Moreover, we add an attention mechanism to improve the scalability and the accuracy of the CNN-LSTM model. In the experiments, we compare our proposed model with different approaches in two real stock datasets. The results confirm the efficiency and scalability of our proposed method.
Year
DOI
Venue
2019
10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00176
IEEE 17TH INT CONF ON DEPENDABLE, AUTONOM AND SECURE COMP / IEEE 17TH INT CONF ON PERVAS INTELLIGENCE AND COMP / IEEE 5TH INT CONF ON CLOUD AND BIG DATA COMP / IEEE 4TH CYBER SCIENCE AND TECHNOLOGY CONGRESS (DASC/PICOM/CBDCOM/CYBERSCITECH)
Keywords
Field
DocType
stock prediction, multi-factor, CNN, LSTM, attention mechanism
Data mining,Time series,Data modeling,Convolutional neural network,Computer science,Recurrent neural network,Feature extraction,Hybrid neural network,Artificial neural network,Scalability
Conference
Citations 
PageRank 
References 
1
0.35
0
Authors
6
Name
Order
Citations
PageRank
Chen Li12816196.16
Xu Zhang210.35
Mahboob Qaosar332.40
Saleh Ahmed422.39
Kazi Md. Rokibul Alam510.69
Yasuhiko Morimoto6528341.88