Final published version, 2.11 MB, PDF document
Research output: Contribution to Journal/Magazine › Journal article
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
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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 -