Neural Spatio-Temporal Point Processes

Tian Qi Chen · Brandon Amos · Maximilian Nickel

Keywords: [ point processes ] [ differential equations ] [ Normalizing flows ]

[ Abstract ]
[ Paper ]
Thu 6 May 9 a.m. PDT — 11 a.m. PDT


We propose a new class of parameterizations for spatio-temporal point processes which leverage Neural ODEs as a computational method and enable flexible, high-fidelity models of discrete events that are localized in continuous time and space. Central to our approach is a combination of continuous-time neural networks with two novel neural architectures, \ie, Jump and Attentive Continuous-time Normalizing Flows. This approach allows us to learn complex distributions for both the spatial and temporal domain and to condition non-trivially on the observed event history. We validate our models on data sets from a wide variety of contexts such as seismology, epidemiology, urban mobility, and neuroscience.

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