Research on Stock Trend Prediction Based on Improved LSTM Model

DOI

10.2991/978-94-6463-010-7_76How to use a DOI?

Keywords

GRU; Attention Mechanism; CNN; LSTM; Factor Correlation Analysis; Stock Forecasting

Abstract

Aiming at the problem that deep features of stock data are difficult to extract and the prediction accuracy is not high, an improved LSTM model CGLA is constructed. Firstly, the RNN-Attention model, LSTM-Attention model and GRU-Attention model are constructed by using attention mechanism. GRU-Attention model with the best performance is selected by comparison. The deep features of stock time series data are extracted by CNN and sent to GRU-Attention model. Then LSTM is used to improve the network structure of the above training model, based on this, a hybrid CGLA model (CNN-GRU-LSTM-Attention) is constructed to predict the stock price of CSI300. After experimental verification, the MSE of CGLA model is reduced by two orders of magnitude compared with the comparison model, the R2_score is significantly improved, and the running time of CGLA model is greatly shortened compared with the comparison model. This paper also integrated factor correlation analysis, in a number of stock indicators in a comprehensive analysis of the closing price of the relevant stock indicators, combined with CGLA model to predict. The experimental results show that the combination of deep learning model and stock index influence factors can make the experiment obtain more accurate and more real stock trend prediction results.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Qianqian Zhang
AU  - Tianhua Lin
AU  - Rongmei Zhang
AU  - Xia Zhao
PY  - 2022
DA  - 2022/12/02
TI  - Research on Stock Trend Prediction Based on Improved LSTM Model
BT  - Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022)
PB  - Atlantis Press
SP  - 742
EP  - 756
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-010-7_76
DO  - 10.2991/978-94-6463-010-7_76
ID  - Zhang2022
ER  -