Neural Spatio-Temporal Point Processes

Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

Keywords: point processes, normalizing flows, differential equations

Abstract: 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.

One-sentence Summary: We motivate the use of Continuous-time Normalizing Flows for building spatio-temporal point processes, and discuss modeling conditional dependencies with recurrent- or attention-based Neural ODEs.

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Code: [ facebookresearch/neural_stpp](https://github.com/facebookresearch/neural_stpp)

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2011.04583/code)