[2603.23746] Kronecker-Structured Nonparametric Spatiotemporal Point Processes

Computer Science > Machine Learning arXiv:2603.23746 (cs) [Submitted on 24 Mar 2026] Title:Kronecker-Structured Nonparametric Spatiotemporal Point Processes Authors:Zhitong Xu, Qiwei Yuan, Yinghao Chen, Yan Sun, Bin Shen, Shandian Zhe View a PDF of the paper titled Kronecker-Structured Nonparametric Spatiotemporal Point Processes, by Zhitong Xu and 5 other authors View PDF HTML (experimental) Abstract:Events in spatiotemporal domains arise in numerous real-world applications, where uncovering event relationships and enabling accurate prediction are central challenges. Classical Poisson and Hawkes processes rely on restrictive parametric assumptions that limit their ability to capture complex interaction patterns, while recent neural point process models increase representational capacity but integrate event information in a black-box manner, hindering interpretable relationship discovery. To address these limitations, we propose a Kronecker-Structured Nonparametric Spatiotemporal Point Process (KSTPP) that enables transparent event-wise relationship discovery while retaining high modeling flexibility. We model the background intensity with a spatial Gaussian process (GP) and the influence kernel as a spatiotemporal GP, allowing rich interaction patterns including excitation, inhibition, neutrality, and time-varying effects. To enable scalable training and prediction, we adopt separable product kernels and represent the GPs on structured grids, inducing Kronecker-structured...