Detecting Anomalous Event Sequences with Temporal Point Processes
This work addresses anomaly detection in event data for domains like healthcare and security, presenting an incremental improvement with a novel test for temporal point processes.
The paper tackles the problem of detecting anomalous continuous-time event sequences by framing it as out-of-distribution detection for temporal point processes, proposing a new goodness-of-fit test that addresses limitations of existing methods and excels in experiments on simulated and real-world data.
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.