APSO-TA-LSTM: a long and short term memory model combining time attention and adaptive particle swarm optimization for stock forecasting-Reference-Cited by-同舟云学术

APSO-TA-LSTM: a long and short term memory model combining time attention and adaptive particle swarm optimization for stock forecasting

Published:2023-06-14 Issue:7 Volume:52 Page:876-893
ISSN:0308-1079
Container-title:International Journal of General Systems
language:en
Short-container-title:International Journal of General Systems

Author:

Hao Tianyu¹,Song Gang²,Du Hongwei²

Affiliation:

1. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, People's Republic of China

2. School of Mathematics, Shandong University, Jinan, People's Republic of China

Funder

National Natural Science Foundation of China

Shandong Provincial Natural Science Foundation Key Project

Publisher

Informa UK Limited

Subject

Computer Science Applications,Modeling and Simulation,Information Systems,Theoretical Computer Science,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/03081079.2023.2222888

Reference35 articles.

1. A deep learning method DCWR with HANet for stock market prediction using news articles

2. Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2014. “Neural Machine Translation by Jointly Learning to Align and Translate.” arXiv preprint arXiv:1409.0473.

3. LSTM based decision support system for swing trading in stock market

4. A novel graph convolutional feature based convolutional neural network for stock trend prediction

5. Mean–variance portfolio optimization using machine learning-based stock price prediction

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