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Self-supervised generative learning for sequential data prediction

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Abstract

For many real world applications, such as stock price prediction and video frame synthesis, sequential data prediction is a fundamental and challenging task. Considering the temporal features of sequential data, existing approaches generally adopt recurrent neural network and its variants for the prediction. However, for sequences with complex structure, these approaches cannot guarantee to obtain promising results. In this paper, to address the above issue, we formulate sequential data prediction as a self-supervised generative learning problem. Concretely, we design a generator to learn the distribution of the sequential data and generate the predicted values, as well as a discriminator to judge whether or not the input sequential data are real or fake. Based on this proposed framework and the adversarial learning scheme, we develop the corresponding networks for vector inputs and high-order tensor inputs, respectively, which are respectively named vector generative network (VGN) and high-order tensor generative network (HTGN). Extensive experiments on five stock price prediction datasets and two video frame prediction datasets demonstrate the effectiveness of our framework, and its advantages over the state-of-the-art approaches. Our main code and the used data have been shared at https://github.com/xsavagek/SSGL.

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Acknowledgements

This work was partially supported by the National Key Research and Development Program of China under Grant No. 2018AAA0100400, HY Project under Grant No. LZY2022033004, the Natural Science Foundation of Shandong Province under Grants No. ZR2020MF131 and No. ZR2021ZD19, Project of the Marine Science and Technology cooperative Innovation Center under Grant No. 22-05-CXZX-04-03-17, the Science and Technology Program of Qingdao under Grant No. 21-1-4-ny-19-nsh, and Project of Associative Training of Ocean University of China under Grant No. 202265007.

We want to thank “Qingdao AI Computing Center” and “Eco-Innovation Center” for providing inclusive computing power and technical support of MindSpore during the completion of this paper.

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Authors and Affiliations

  1. College of Computer Science and Technology, Ocean University of China, Qingdao, 266100, China

    Ke Xu, Guoqiang Zhong & Zhaoyang Deng

  2. Graduate School of Systems Life Sciences, Kyushu University, Fukuok, 819-0395, Japan

    Kang Zhang

  3. Data Science Research Center & Division of Natural and Applied Sciences, Duke Kunshan University, Suzhou, 215316, China

    Kaizhu Huang

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  1. Ke Xu
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Correspondence to Guoqiang Zhong or Kaizhu Huang.

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Xu, K., Zhong, G., Deng, Z. et al. Self-supervised generative learning for sequential data prediction. Appl Intell 53, 20675–20689 (2023). https://doi.org/10.1007/s10489-023-04578-5

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