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anomaly: Detection of Anomalous Structure in Time Series Data

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

Forthcoming

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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, 21.12.2023.

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

Harvard

Fisch, A, Grose, D, Eckley, IA, Fearnhead, P & Bardwell, L 2023, 'anomaly: Detection of Anomalous Structure in Time Series Data', Journal of Statistical Software.

APA

Fisch, A., Grose, D., Eckley, I. A., Fearnhead, P., & Bardwell, L. (in press). anomaly: Detection of Anomalous Structure in Time Series Data. Journal of Statistical Software.

Vancouver

Fisch A, Grose D, Eckley IA, Fearnhead P, Bardwell L. anomaly: Detection of Anomalous Structure in Time Series Data. Journal of Statistical Software. 2023 Dec 21.

Author

Fisch, Alex ; Grose, Daniel ; Eckley, Idris A. et al. / anomaly: Detection of Anomalous Structure in Time Series Data. In: Journal of Statistical Software. 2023.

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",
year = "2023",
month = dec,
day = "21",
language = "English",
journal = "Journal of Statistical Software",
issn = "1548-7660",
publisher = "University of California at Los Angeles",

}

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 - 2023/12/21

Y1 - 2023/12/21

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 - Journal of Statistical Software

JF - Journal of Statistical Software

SN - 1548-7660

ER -