The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process
This work addresses predicting event types and timing in continuous-time streams, such as in finance or social media, with incremental improvements in modeling flexibility.
The paper tackles modeling streams of discrete events in continuous time by introducing a neurally self-modulating multivariate point process using a continuous-time LSTM, achieving competitive likelihood and predictive accuracy on real and synthetic datasets.
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.