anomaly: Detection of Anomalous Structure in Time Series Data

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

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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.

Item Type:
Journal Article
Journal or Publication Title:
arxiv.org
Additional Information:
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
Subjects:
ID Code:
149680
Deposited By:
Deposited On:
07 Dec 2020 12:05
Refereed?:
No
Published?:
Published
Last Modified:
17 Jun 2021 07:52