stray: Anomaly Detection in High Dimensional and Temporal Data (original) (raw)
This is a modification of 'HDoutliers' package. The 'HDoutliers' algorithm is a powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation. However, it suffers from some limitations that significantly hinder its performance level, under certain circumstances. This package implements the algorithm proposed in Talagala, Hyndman and Smith-Miles (2019) <doi:10.48550/arXiv.1908.04000> for detecting anomalies in high-dimensional data that addresses these limitations of 'HDoutliers' algorithm. We define an anomaly as an observation that deviates markedly from the majority with a large distance gap. An approach based on extreme value theory is used for the anomalous threshold calculation.
Version: | 0.1.1 |
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Depends: | R (≥ 3.4.0) |
Imports: | FNN, ggplot2, colorspace, pcaPP, stats, ks |
Published: | 2020-06-29 |
DOI: | 10.32614/CRAN.package.stray |
Author: | Priyanga Dilini Talagala [aut, cre], Rob J Hyndman [ths], Kate Smith-Miles [ths] |
Maintainer: | Priyanga Dilini Talagala |
BugReports: | https://github.com/pridiltal/stray/issues |
License: | GPL-2 |
NeedsCompilation: | no |
CRAN checks: | stray results |
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