Abstract
Stock data have a long memory, that is, changes in stock prices are closely related to historical transaction data. Also, Recurrent Neural Networks have good time series feature extraction capabilities. The paper proposed prediction models based on RNN/LSTM/GRU respectively. The attention mechanism has the ability to select and focus "key information”. Therefore, based on the conventional Recurrent Neural Network, this paper introduced the attention mechanism and proposed a prediction model based on AT-RNN/AT-LSTM/AT-GRU. And the paper modeled and experimented with it. The results showed that: (1) In the most basic comparison test of RNN-M, LSTM-M, and GRU-M prediction models, the GRU-M and LSTM -M was significantly better than the RNN-M and the GRU-M was slightly better than the LSTM-M; (2) The introduction of the attention mechanism layer was helpful to improve the accuracy of the stock fluctuation prediction model;(3) Deeper neural networks did not necessarily achieve better results.
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Acknowledgements
This work was supported by National Natural Science Fund (71672128), National Key Research and Development Program of China (2018YFC0830400), Shanghai Natural Science Foundation (19ZR1435600), and Natural Science Fostering Foundation (1F-19-303-001).
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Zhao, J., Zeng, D., Liang, S. et al. Prediction model for stock price trend based on recurrent neural network. J Ambient Intell Human Comput 12, 745–753 (2021). https://doi.org/10.1007/s12652-020-02057-0
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DOI: https://doi.org/10.1007/s12652-020-02057-0