Continuously-observed event occurrences, often exhibit self- and mutually-exciting effects, which can be well modeled using temporal point processes. Beyond that, these event dynamics may also change over time, with certain periodic trends. We propose a novel variational auto-encoder to capture such a mixture of temporal dynamics. More specifically, the whole time interval of the input sequence is partitioned into a set of sub-intervals. The event dynamics are assumed to be stationary within each sub-interval, but could be changing across those sub-intervals. In particular, we use a sequential latent variable model to learn a dependency graph between the observed dimensions, for each sub-interval. The model predicts the future event times, by using the learned dependency graph to remove the noncontributing influences of past events. By doing so, the proposed model demonstrates its higher accuracy in predicting inter-event times and event types for several real-world event sequences, compared with existing state of the art neural point processes.

翻译：连续观测的事件发生通常表现出自激发与互激发效应，这可通过时序点过程进行有效建模。除此之外，这些事件动态也可能随时间变化，并呈现某些周期性趋势。本文提出一种新型变分自编码器以捕捉这类混合时间动态。具体而言，输入序列的完整时间区间被划分为若干子区间，假定事件动态在每个子区间内保持平稳，但可在不同子区间之间发生变化。我们特别采用序列潜变量模型，为每个子区间学习观测维度间的依赖图。该模型通过利用学习到的依赖图消除过去事件中的非贡献性影响，从而预测未来事件时间。通过这种方式，与现有最先进的神经点过程相比，所提模型在多个真实事件序列的预测中展现出更高的间隔时间与事件类型预测精度。