anomaly: Detection of Anomalous Structure in Time Series Data

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jss4257
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https://doi.org/10.18637/jss.v110.i01
Final published version
Available under license: CC BY

Keywords

anomaly, detection, point anomaly, collective anomaly, BARD, CAPA, PASS

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Research output: Contribution to Journal/Magazine › Journal article › peer-review

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anomaly: Detection of Anomalous Structure in Time Series Data. / Fisch, Alex ; Grose, Daniel ; Eckley, Idris A. et al.
In: Journal of Statistical Software, Vol. 110, No. 1, 1, 29.08.2024, p. 1-24.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Bibtex

@article{eb0557b650b34c02af1aaefdc3322695,

title = "anomaly: Detection of Anomalous Structure in Time Series Data",

abstract = " One of the contemporary challenges in anomaly detection is the ability to detect, and differentiate between, both point and collective anomalies within a data sequence or time series. The anomaly package has been developed to provide users with a choice of anomaly detection methods and, in particular, provides an implementation of the recently proposed CAPA family of anomaly detection algorithms. This article describes the methods implemented whilst also highlighting their application to simulated data as well as real data examples contained in the package. ",

keywords = "anomaly, detection, point anomaly, collective anomaly, BARD, CAPA, PASS",

author = "Alex Fisch and Daniel Grose and Eckley, {Idris A.} and Paul Fearnhead and Lawrence Bardwell",

year = "2024",

month = aug,

day = "29",

doi = "10.18637/jss.v110.i01",

language = "English",

volume = "110",

pages = "1--24",

journal = "Journal of Statistical Software",

issn = "1548-7660",

publisher = "University of California at Los Angeles",

number = "1",

}

RIS

TY - JOUR

T1 - anomaly: Detection of Anomalous Structure in Time Series Data

AU - Fisch, Alex

AU - Grose, Daniel

AU - Eckley, Idris A.

AU - Fearnhead, Paul

AU - Bardwell, Lawrence

PY - 2024/8/29

Y1 - 2024/8/29

N2 - One of the contemporary challenges in anomaly detection is the ability to detect, and differentiate between, both point and collective anomalies within a data sequence or time series. The anomaly package has been developed to provide users with a choice of anomaly detection methods and, in particular, provides an implementation of the recently proposed CAPA family of anomaly detection algorithms. This article describes the methods implemented whilst also highlighting their application to simulated data as well as real data examples contained in the package.

AB - One of the contemporary challenges in anomaly detection is the ability to detect, and differentiate between, both point and collective anomalies within a data sequence or time series. The anomaly package has been developed to provide users with a choice of anomaly detection methods and, in particular, provides an implementation of the recently proposed CAPA family of anomaly detection algorithms. This article describes the methods implemented whilst also highlighting their application to simulated data as well as real data examples contained in the package.

KW - anomaly

KW - detection

KW - point anomaly

KW - collective anomaly

KW - BARD

KW - CAPA

KW - PASS

U2 - 10.18637/jss.v110.i01

DO - 10.18637/jss.v110.i01

M3 - Journal article

VL - 110

SP - 1

EP - 24

JO - Journal of Statistical Software

JF - Journal of Statistical Software

SN - 1548-7660

IS - 1

M1 - 1

ER -

Research

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