Detecting Anomalous Event Sequences with Temporal Point Processes

Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

7 Zitate (Scopus)

Abstract

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.

OriginalspracheEnglisch
TitelAdvances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021
Redakteure/-innenMarc'Aurelio Ranzato, Alina Beygelzimer, Yann Dauphin, Percy S. Liang, Jenn Wortman Vaughan
Herausgeber (Verlag)Neural information processing systems foundation
Seiten13419-13431
Seitenumfang13
ISBN (elektronisch)9781713845393
PublikationsstatusVeröffentlicht - 2021
Veranstaltung35th Conference on Neural Information Processing Systems, NeurIPS 2021 - Virtual, Online
Dauer: 6 Dez. 202114 Dez. 2021

Publikationsreihe

NameAdvances in Neural Information Processing Systems
Band16
ISSN (Print)1049-5258

Konferenz

Konferenz35th Conference on Neural Information Processing Systems, NeurIPS 2021
OrtVirtual, Online
Zeitraum6/12/2114/12/21

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