anomaly: Detection of Anomalous Structure in Time Series Data

Fisch, Alex and Grose, Daniel and Eckley, Idris A. and Fearnhead, Paul and Bardwell, Lawrence (2023) anomaly: Detection of Anomalous Structure in Time Series Data. Journal of Statistical Software. ISSN 1548-7660 (In Press)

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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:
Journal of Statistical Software
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1712
Subjects:
?? stat.apsoftwarestatistics and probabilitystatistics, probability and uncertainty ??
ID Code:
213713
Deposited By:
Deposited On:
29 Jan 2024 16:45
Refereed?:
Yes
Published?:
In Press
Last Modified:
01 May 2024 23:30