  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.
