Detecting Anomalous Event Sequences with Temporal Point Processes

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

Detecting Anomalous Event Sequences with Temporal Point Processes

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

Informatics 26 - Chair of Professorship of Data Analytics and Machine Learning

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

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.

Original language	English
Title of host publication	Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021
Editors	Marc'Aurelio Ranzato, Alina Beygelzimer, Yann Dauphin, Percy S. Liang, Jenn Wortman Vaughan
Publisher	Neural information processing systems foundation
Pages	13419-13431
Number of pages	13
ISBN (Electronic)	9781713845393
State	Published - 2021
Event	35th Conference on Neural Information Processing Systems, NeurIPS 2021 - Virtual, Online Duration: 6 Dec 2021 → 14 Dec 2021

Publication series

Name	Advances in Neural Information Processing Systems
Volume	16
ISSN (Print)	1049-5258

Conference

Conference	35th Conference on Neural Information Processing Systems, NeurIPS 2021
City	Virtual, Online
Period	6/12/21 → 14/12/21

Cite this

Shchur, O., Türkmen, A. C., Januschowski, T., Gasthaus, J., & Günnemann, S. (2021). Detecting Anomalous Event Sequences with Temporal Point Processes. In MA. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021 (pp. 13419-13431). (Advances in Neural Information Processing Systems; Vol. 16). Neural information processing systems foundation.

Shchur, Oleksandr ; Türkmen, Ali Caner ; Januschowski, Tim et al. / Detecting Anomalous Event Sequences with Temporal Point Processes. Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. editor / Marc'Aurelio Ranzato ; Alina Beygelzimer ; Yann Dauphin ; Percy S. Liang ; Jenn Wortman Vaughan. Neural information processing systems foundation, 2021. pp. 13419-13431 (Advances in Neural Information Processing Systems).

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title = "Detecting Anomalous Event Sequences with Temporal Point Processes",

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.",

author = "Oleksandr Shchur and T{\"u}rkmen, {Ali Caner} and Tim Januschowski and Jan Gasthaus and Stephan G{\"u}nnemann",

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year = "2021",

language = "English",

series = "Advances in Neural Information Processing Systems",

publisher = "Neural information processing systems foundation",

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editor = "Marc'Aurelio Ranzato and Alina Beygelzimer and Yann Dauphin and Liang, {Percy S.} and {Wortman Vaughan}, Jenn",

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Shchur, O, Türkmen, AC, Januschowski, T, Gasthaus, J & Günnemann, S 2021, Detecting Anomalous Event Sequences with Temporal Point Processes. in MA Ranzato, A Beygelzimer, Y Dauphin, PS Liang & J Wortman Vaughan (eds), Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Advances in Neural Information Processing Systems, vol. 16, Neural information processing systems foundation, pp. 13419-13431, 35th Conference on Neural Information Processing Systems, NeurIPS 2021, Virtual, Online, 6/12/21.

Detecting Anomalous Event Sequences with Temporal Point Processes. / Shchur, Oleksandr; Türkmen, Ali Caner; Januschowski, Tim et al.
Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. ed. / Marc'Aurelio Ranzato; Alina Beygelzimer; Yann Dauphin; Percy S. Liang; Jenn Wortman Vaughan. Neural information processing systems foundation, 2021. p. 13419-13431 (Advances in Neural Information Processing Systems; Vol. 16).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Detecting Anomalous Event Sequences with Temporal Point Processes

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AU - Türkmen, Ali Caner

AU - Januschowski, Tim

AU - Gasthaus, Jan

AU - Günnemann, Stephan

PY - 2021

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AB - 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.

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Shchur O, Türkmen AC, Januschowski T, Gasthaus J, Günnemann S. Detecting Anomalous Event Sequences with Temporal Point Processes. In Ranzato MA, Beygelzimer A, Dauphin Y, Liang PS, Wortman Vaughan J, editors, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Neural information processing systems foundation. 2021. p. 13419-13431. (Advances in Neural Information Processing Systems).

Detecting Anomalous Event Sequences with Temporal Point Processes

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