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GTHP: a novel graph transformer Hawkes process for spatiotemporal event prediction

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Abstract

The event sequences with spatiotemporal characteristics have been rapidly produced in various domains, such as earthquakes in seismology, electronic medical records in healthcare, and transactions in the financial market. These data often continue for weeks, months, or years, and the past events may trigger subsequent events. In this context, modeling the spatiotemporal event sequences and forecasting the next event has become a hot topic. However, existing models either failed to capture the long-term temporal dependencies or ignored the essential spatial information between sequences. In this paper, we proposed a novel graph transformer Hawkes process (GTHP) model to capture the long-term temporal dependencies and spatial information from historical events. The core concept of GTHP is to learn the spatial information by graph convolutional neural networks and capture long-term temporal dependencies from events embedding by self-attention mechanism. Moreover, we integrated the learned spatial information into the event embedding as auxiliary information. Numerous experiments on synthetic and real-world datasets proved the effectiveness of the proposed model.

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Data Availability

The data supporting this study’s findings are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China

    Yiman Xie & Jianbin Wu

  2. State Grid Wenzhou Electric Power Supply Company, Wenzhou, 325100, Zhejiang, China

    Yan Zhou

Authors
  1. Yiman Xie
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  2. Jianbin Wu
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  3. Yan Zhou
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Contributions

Yiman Xie contributed to the conception of the study; Jianbin Wu contributed to the conception of the study; Yiman Xie performed the experiment; Yiman Xie contributed significantly to analysis and manuscript preparation; Jianbin Wu contributed significantly to analysis and manuscript preparation; Yiman Xie performed the data analyses and wrote the manuscript; Jianbin Wu helped perform the analysis with constructive discussions. Yan Zhou contributed substantially to the paper’s ablation study, experimentation, and revision.

Corresponding author

Correspondence to Jianbin Wu.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work. There is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

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Xie, Y., Wu, J. & Zhou, Y. GTHP: a novel graph transformer Hawkes process for spatiotemporal event prediction. Knowl Inf Syst (2024). https://doi.org/10.1007/s10115-024-02080-z

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  • DOI: https://doi.org/10.1007/s10115-024-02080-z

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