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Anomaly Detection in High Dimensional and Temporal Data
Description
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.