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stray: Anomaly Detection in High Dimensional and Temporal Data

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
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 <pritalagala at gmail.com>
BugReports:	https://github.com/pridiltal/stray/issues
License:	GPL-2
NeedsCompilation:	no
In views:	AnomalyDetection
CRAN checks:	stray results

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