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DeepAR-Attention probabilistic prediction for stock price series

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Abstract

Stock price prediction is a significant research domain, intersecting statistics, finance, and economics. Accurately forecasting stock price trends has always been a focal point for many researchers. However, traditional statistical methods for time series prediction still lack accuracy. The existing deep learning-based methods for stock price prediction have significantly enhanced the accuracy of predicting individual stock prices. However, they are not effective in forecasting the probability range of future stock price trends. In this paper, to address these limitations, we propose a novel DeepAR model based on the attention mechanism (DeepARA) for both single-point and probabilistic predictions of stock prices. This enhances the accuracy and flexibility of stock price forecasting. Although the attention mechanism was initially developed for natural language processing, it has now found applications in time series forecasting, including the dynamics of the stock market. Attention allocates different weights to time points of varying importance, thereby enhancing the model’s ability to capture fundamental market dynamics. We conducted multiple experiments in the Chinese stock market, involving 30 stocks across the top six sectors. Compared with baseline models, the DeepARA model demonstrates superior predictive capabilities.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgments

This work was supported in part by grants from National Science Foundation of China (61571005) and the fundamental research program of Guangdong, China (2020B1515310023, 2023A1515011281).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510640, Guangdong province, China

    Jiacheng Li, Zhiheng Zhou, Junmei Yang & Delu Zeng

  2. School of Mathematics, South China University of Technology, Guangzhou, 510640, Guangdong province, China

    Wei Chen

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  1. Jiacheng Li
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  2. Wei Chen
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  5. Delu Zeng
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Contributions

JL contributed to conception and design, data collection, software, analysis and interpretation of results, and writing—original and editing. WC contributed to data visualization, program modification, and writing—reviewing draft. ZZ contributed to writing—reviewing draft and supervision. JY contributed to writing—reviewing draft. DZ contributed to analysis and interpretation of results, writing—review draft, and supervision.

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Correspondence to Delu Zeng.

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Li, J., Chen, W., Zhou, Z. et al. DeepAR-Attention probabilistic prediction for stock price series. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09916-3

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