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anomaly: Detection of Anomalous Structure in Time Series Data

Research output: Contribution to Journal/Magazine › Journal article

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anomaly: Detection of Anomalous Structure in Time Series Data. / Fisch, Alex; Grose, Daniel; Eckley, Idris A.; Fearnhead, Paul; Bardwell, Lawrence.

In: arxiv.org, 19.10.2020.

Research output: Contribution to Journal/Magazine › Journal article

Harvard

Fisch, A, Grose, D, Eckley, IA, Fearnhead, P & Bardwell, L 2020, 'anomaly: Detection of Anomalous Structure in Time Series Data', arxiv.org.

APA

Fisch, A., Grose, D., Eckley, I. A., Fearnhead, P., & Bardwell, L. (2020). anomaly: Detection of Anomalous Structure in Time Series Data. arxiv.org.

Vancouver

Fisch A, Grose D, Eckley IA, Fearnhead P, Bardwell L. anomaly: Detection of Anomalous Structure in Time Series Data. arxiv.org. 2020 Oct 19.

Author

Fisch, Alex ; Grose, Daniel ; Eckley, Idris A. ; Fearnhead, Paul ; Bardwell, Lawrence. / anomaly: Detection of Anomalous Structure in Time Series Data. In: arxiv.org. 2020.

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 = "stat.AP",
author = "Alex Fisch and Daniel Grose and Eckley, {Idris A.} and Paul Fearnhead and Lawrence Bardwell",
note = "31 pages, 10 figures. An R package that implements the methods discussed in the paper can be obtained from The Comprehensive R Archive Network (CRAN) via https://cran.r-project.org/web/packages/anomaly/index.html",
year = "2020",
month = oct,
day = "19",
language = "English",
journal = "arxiv.org",

}

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

N1 - 31 pages, 10 figures. An R package that implements the methods discussed in the paper can be obtained from The Comprehensive R Archive Network (CRAN) via https://cran.r-project.org/web/packages/anomaly/index.html

PY - 2020/10/19

Y1 - 2020/10/19

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 - stat.AP

M3 - Journal article

JO - arxiv.org

JF - arxiv.org

ER -