  Asynchronous events on the continuous time domain, e.g., social media actions
and stock transactions, occur frequently in the world. The ability to recognize
occurrence patterns of event sequences is crucial to predict which typeof
events will happen next and when. A de facto standard mathematical framework to
do this is the Hawkes process. In order to enhance expressivity of multivariate
Hawkes processes, conventional statistical methods and deep recurrent networks
have been employed to modify its intensity function. The former is highly
interpretable and requires small size of training data but relies on correct
model design while the latter has less dependency on prior knowledge and is
more powerful in capturing complicated patterns. We leverage pros and cons of
these models and propose a self-attentive Hawkes process(SAHP). The proposed
method adapts self-attention to fit the intensity function of Hawkes processes.
This design has two benefits:(1) compared with conventional statistical
methods, the SAHP is more powerful to identify complicated dependency
relationships between temporal events; (2)compared with deep recurrent
networks, the self-attention mechanism is able to capture longer historical
information, and is more interpretable because the learnt attention weight
tensor shows contributions of each historical event. Experiments on four
real-world datasets demonstrate the effectiveness of the proposed method.
