  Modern data acquisition routinely produce massive amounts of event sequence
data in various domains, such as social media, healthcare, and financial
markets. These data often exhibit complicated short-term and long-term temporal
dependencies. However, most of the existing recurrent neural network based
point process models fail to capture such dependencies, and yield unreliable
prediction performance. To address this issue, we propose a Transformer Hawkes
Process (THP) model, which leverages the self-attention mechanism to capture
long-term dependencies and meanwhile enjoys computational efficiency. Numerical
experiments on various datasets show that THP outperforms existing models in
terms of both likelihood and event prediction accuracy by a notable margin.
Moreover, THP is quite general and can incorporate additional structural
knowledge. We provide a concrete example, where THP achieves improved
prediction performance for learning multiple point processes when incorporating
their relational information.
