  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 simultaneous 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.
