find_theshold

<p>A vector of outlier scores. Can be a named vector or a vector with no names.</p>

outlier_score

<p>Threshold for determining the cutoff for outliers. Observations are considered
outliers if they fall in the \((1- alpha)\) tail of the distribution of the nearest-neighbor
distances between exemplars.</p>

alpha

outtail


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.

Priyanga Dilini Talagala

stray

Anomaly Detection in High Dimensional and Temporal Data

Rob J Hyndman

Kate Smith-Miles

find_theshold function

Find Outlier Threshold — find_theshold

Find Outlier Threshold

find_theshold: Find Outlier Threshold

Description

Usage

Arguments

Value