hawkes processes are a popular framework to model the occurrence of
sequential events, i.e., occurrence dynamics, in several fields such as social
diffusion. In real-world scenarios, the inter-arrival time among events is
irregular. However, existing neural network-based Hawkes process
提出了一种利用 self-attention 机制进行 intensity function 拟合的 self-attentive Hawkes process 方法，相较于传统的统计方法和深度循环神经网络，该方法能更好地识别时间事件之间的复杂依赖关系，并且能够捕捉更长的历史信息，可以针对多变量事件序列进行有效的复杂模式预测。