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 Collective And Point Anomaly 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.
Cite
@article{arxiv.2010.09353,
title = {anomaly : Detection of Anomalous Structure in Time Series Data},
author = {Alex Fisch and Daniel Grose and Idris A. Eckley and Paul Fearnhead and Lawrence Bardwell},
journal= {arXiv preprint arXiv:2010.09353},
year = {2024}
}
Comments
24 pages, 6 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