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...

Descripción completa

Detalles Bibliográficos

Autores principales:	An Luo, Liang Zhong, Jianglin Wang, Yue Wang, Shaojie Li, Weipeng Tai
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	IEEE 2024-01-01
Colección:	IEEE Access
Materias:	Multiple factor stock correlation prediction time series prediction attention mechanism CNN BiLSTM
Acceso en línea:	https://ieeexplore.ieee.org/document/10444524/
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!