Communication Dans Un Congrès Année : 2025

Numerically Efficient Parametric Inference for Learning Space-Time Hawkes Processes

Résumé

In a wide range of spatio-temporal datasets, from sociology to seismology, self-exciting dynamics are often observed, characterized by event triggering and clustering across both space and time. Space-time Hawkes processes provide a powerful framework to model such phenomena. This paper introduces a flexible parametric inference method to estimate the underlying kernel parameters involved in the intensity function of a space-time Hawkes process based on such data. Our approach combines three core components: 1) kernels with finite support, 2) discretization of the space-time domain, and 3) efficient (possibly approximate) precomputations. The inference method we propose then relies on a gradient-based solver that offers both computational efficiency and strong statistical performance. Alongside a detailed presentation of the algorithmic framework, we present numerical experiments on synthetic and real spatio-temporal data, offering solid empirical evidence of the validity and applicability of the proposed methodology.

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hal-05409023 , version 1 (07-01-2026)

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Emilia Siviero, Guillaume Staerman, Stéphan Clémençon, Thomas Moreau. Numerically Efficient Parametric Inference for Learning Space-Time Hawkes Processes. Proceedings of the 12th IEEE International Conference on Data Science and Advanced Analytics (DSAA), Oct 2025, Birmingham, United Kingdom. pp.1-10, ⟨10.1109/DSAA65442.2025.11247997⟩. ⟨hal-05409023⟩
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