# 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 No Results!