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) <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.

Getting started

Package details

AuthorPriyanga Dilini Talagala [aut, cre] (<>), Rob J Hyndman [ths] (<>), Kate Smith-Miles [ths]
MaintainerPriyanga Dilini Talagala <>
Package repositoryView on CRAN
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stray documentation built on July 2, 2020, 4:03 a.m.