Automatically detecting anomalies in event data can provide substantial value in domains such as healthcare, DevOps, and information security. In this paper, we frame the problem of detecting anomalous continuous-time event sequences as out-of-distribution (OoD) detection for temporal point processes (TPPs). First, we show how this problem can be approached using goodness-of-fit (GoF) tests. We then demonstrate the limitations of popular GoF statistics for TPPs and propose a new test that addresses these shortcomings. The proposed method can be combined with various TPP models, such as neural TPPs, and is easy to implement. In our experiments, we show that the proposed statistic excels at both traditional GoF testing, as well as at detecting anomalies in simulated and real-world data.

翻译：如果数据能自动发现异常情况,则在保健、发展轨道和信息安全等领域可以提供大量价值。在本文中,我们将发现异常连续时间事件序列的问题定义为用于时间点过程的超分配(OoD)检测。首先,我们展示了如何利用良好的测试(GoF)来解决这一问题。然后,我们展示了受欢迎的TPP政府统计数据的局限性,并提出了解决这些缺陷的新测试方案。拟议方法可以与各种TPP模型(如神经TPP)相结合,并且容易实施。在我们的实验中,我们展示了拟议的统计在传统的GoF测试以及模拟和真实世界数据的异常中都很优秀。