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) 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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("stray")

0.1.0 by Priyanga Dilini Talagala, a month ago


Report a bug at https://github.com/pridiltal/stray/issues


Browse source code at https://github.com/cran/stray


Authors: Priyanga Dilini Talagala [aut, cre] , Rob J Hyndman [ths] , Kate Smith-Miles [ths]


Documentation:   PDF Manual  


GPL-2 license


Imports FNN, ggplot2, colorspace, pcaPP, stats, ks


See at CRAN