stray v0.1.0

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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.

Functions in stray

Name Description
data_d A wheel dataset with two inliers
use_KNN Find outliers using kNN distance with maximum gap
stray stray: A package for robust anomaly detection in data streams with concept drift
wheel1 wheel data set with inlier and outlier.
find_HDoutliers Detect Anomalies in High Dimensional Data.
display_HDoutliers Display outliers with a scatterplot
data_a A dataset with an outlier
data_b A bimodal dataset with a micro cluster
find_theshold Find Outlier Threshold
ped_data Dataset with pedestrian counts
data_e A bimodal dataset with an inlier
data_f A dataset with an outlier
data_c A dataset with local anomalies and micro clusters
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Details

Type Package
BugReports https://github.com/pridiltal/stray/issues
License GPL-2
Encoding UTF-8
LazyData true
RoxygenNote 6.1.1
NeedsCompilation no
Packaged 2019-12-12 10:05:20 UTC; priyangatalagala
Repository CRAN
Date/Publication 2019-12-17 11:50:03 UTC

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