Neural multi-event forecasting on spatio-temporal point processes using probabilistically enriched transformers

• URL: http://arxiv.org/abs/2211.02922v1
• Date: Sat, 5 Nov 2022 14:55:36 GMT
• Title: Neural multi-event forecasting on spatio-temporal point processes using probabilistically enriched transformers
• Authors: Negar Erfanian, Santiago Segarra, Maarten de Hoop
• Abstract summary: Predicting discrete events in time and space has many scientific applications, such as predicting hazardous earthquakes and outbreaks of infectious diseases. In this architecture we propose a new neural multi-event forecasting of-temporal point processes, utilizing augmented with normalizing flows and layers. Our network makes batched predictions of complex future discrete events, achieving state-the-art performance on a variety of benchmark datasets.
• Score: 18.66217537327045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting discrete events in time and space has many scientific applications, such as predicting hazardous earthquakes and outbreaks of infectious diseases. History-dependent spatio-temporal Hawkes processes are often used to mathematically model these point events. However, previous approaches have faced numerous challenges, particularly when attempting to forecast one or multiple future events. In this work, we propose a new neural architecture for multi-event forecasting of spatio-temporal point processes, utilizing transformers, augmented with normalizing flows and probabilistic layers. Our network makes batched predictions of complex history-dependent spatio-temporal distributions of future discrete events, achieving state-of-the-art performance on a variety of benchmark datasets including the South California Earthquakes, Citibike, Covid-19, and Hawkes synthetic pinwheel datasets. More generally, we illustrate how our network can be applied to any dataset of discrete events with associated markers, even when no underlying physics is known.

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