Neural Spatio-Temporal Point Processes

• URL: http://arxiv.org/abs/2011.04583v3
• Date: Thu, 18 Mar 2021 00:00:23 GMT
• Title: Neural Spatio-Temporal Point Processes
• Authors: Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
• Abstract summary: We propose a new class of parameterizations for point-trivial processes which leverage Neural ODEs as a computational method. We validate our models on data sets from a wide variety of contexts such as seismology, epidemiology, urban mobility, and neuroscience.
• Score: 31.474420819149724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, i.e., 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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