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Poster
Detecting Anomalous Event Sequences with Temporal Point Processes
Oleksandr Shchur · Ali Caner Turkmen · Tim Januschowski · Jan Gasthaus · Stephan Günnemann

Fri Dec 10 08:30 AM -- 10:00 AM (PST) @ Virtual
in Poster Session 8 »

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.

Keywords: Generative Model

Author Information

Oleksandr Shchur (Technical University of Munich)
Ali Caner Turkmen (Bogazici University)
Tim Januschowski (Amazon Research)

- Director Pricing Platform, Zalando SE - Head of Time Series ML at AWS AI

Jan Gasthaus (Amazon / AWS)
Stephan Günnemann (Technical University of Munich)

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