| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
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