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.

提出了一种基于 Transformer 和 Hawkes 过程的模型（THP），能够更好地捕捉事件序列数据中的短期和长期时间依赖关系，并能够通过集成结构性知识来改善预测表现。