  Many events occur in the world. Some event types are stochastically excited
or inhibited---in the sense of having their probabilities elevated or
decreased---by patterns in the sequence of previous events. Discovering such
patterns can help us predict which type of event will happen next and when. We
model streams of discrete events in continuous time, by constructing a neurally
self-modulating multivariate point process in which the intensities of multiple
event types evolve according to a novel continuous-time LSTM. This generative
model allows past events to influence the future in complex and realistic ways,
by conditioning future event intensities on the hidden state of a recurrent
neural network that has consumed the stream of past events. Our model has
desirable qualitative properties. It achieves competitive likelihood and
predictive accuracy on real and synthetic datasets, including under
missing-data conditions.
