Short-Term Stock Correlation Forecasting Based on CNN-BiLSTM Enhanced by Attention Mechanism

Short-Term Stock Correlation Forecasting Based on CNN-BiLSTM Enhanced by Attention Mechanism

This study utilizes a new approach for short-term stock correlation forecasting using a combination of convolutional neural networks (CNN), bi-directional long and short-term memory (BiLSTM), and attention mechanisms to address the issue of information loss due to excessively long input time series...

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Bibliographic Details
Main Authors: An Luo, Liang Zhong, Jianglin Wang, Yue Wang, Shaojie Li, Weipeng Tai
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Multiple factor
stock correlation prediction
time series prediction
attention mechanism
CNN
BiLSTM
Online Access:https://ieeexplore.ieee.org/document/10444524/

Internet

https://ieeexplore.ieee.org/document/10444524/

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