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.


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0.1.1 by Priyanga Dilini Talagala, a year ago

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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