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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Bibliografiska uppgifter

Huvudupphov:	An Luo, Liang Zhong, Jianglin Wang, Yue Wang, Shaojie Li, Weipeng Tai
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	IEEE 2024-01-01
Serie:	IEEE Access
Ämnen:	Multiple factor stock correlation prediction time series prediction attention mechanism CNN BiLSTM
Länkar:	https://ieeexplore.ieee.org/document/10444524/
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