find_threshold

A vector of outlier scores. Can be a named vector or a vector with no names.

outlier_score

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.

alpha

outtail

Proportion of possible candidates for outliers. This defines the starting point for the
bottom up searching algorithm.

Sample size to calculate an empirical threshold

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.

find_threshold: Find Outlier Threshold

Description

Usage

Arguments

Value