Abstract
With the prosperity of the stock market and the rapid development of artificial intelligence, stock prediction technology has been widely concerned in recent years. Currently, most existing works only focus on stock prediction models via machine learning, ignoring some problems, such as local minima, gradient vanishing and interaction of information at different times. The stock data has characteristics like high noise and nonlinearity, which increase the difficulty of modeling. To solve these problems, we propose the Stock Prediction Model based on LSTM-Attention Network (called SP-LAN). Unlike traditional methods, SP-LAN can train the model with excellent generalization ability. SP-LAN also can make the model better learn interaction of information at different times, thus improving the prediction effectiveness of our model. In addition, we propose a novel feature selection strategy based on embedded importance to reduce the dimensions of data, which makes model train easier. Finally, we conduct experimental studies based on the daily trading dataset of SSE 50. The experimental results demonstrate the effectiveness and efficiency of our model.
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Acknowledgement
This work was supported by the Science and Technology Program Major Project of Liaoning Province of China under Grant No. 2022JH1/10400009, the Natural Science Foundation of Liaoning Province of China under Grant No. 2022-MS-171, 2020-BS-082, the Science Research Fund of Liaoning Province of China under Grant No. LJKZ0094, LJKQZ2021023.
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Sun, J., Li, D., Wang, X., Kou, Y., Li, P., Xie, Y. (2023). SP-LAN: A Stock Prediction Model Based on LSTM-Attention Network. In: Yang, S., Islam, S. (eds) Web and Big Data. APWeb-WAIM 2022 International Workshops. APWeb-WAIM 2022. Communications in Computer and Information Science, vol 1784. Springer, Singapore. https://doi.org/10.1007/978-981-99-1354-1_7
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