stray: stray: A package for robust anomaly detection in data streams...
In stray: Anomaly Detection in High Dimensional and Temporal Data

This package is a modification of HDoutliers package. HDoutliers is a powerful algorithm for the detection of anomalous observations in a dataset, which has (among other advantages) the ability to detect clusters of outliers in multi-dimensional data without requiring a model of the typical behavior of the system. However, it suffers from some limitations that affect its accuracy. In this package, we propose solutions to the limitations of HDoutliers, and propose an extension of the algorithm to deal with data streams that exhibit non-stationary behavior. The results show that our proposed algorithm improves the accuracy, and enables the trade-off between false positives and negatives to be better balanced.

The name stray comes from Search and TRace AnomalY

Talagala, P. D., Hyndman, R. J., & Smith-Miles, K. (2019). Anomaly Detection in High Dimensional Data. https://www.monash.edu/business/ebs/research/publications/ebs/wp20-2019.pdf

Wilkinson, L. (2017). Visualizing big data outliers through distributed aggregation. IEEE transactions on visualization and computer graphics, 24(1), 256-266. https://www.cs.uic.edu/~wilkinson/Publications/outliers.pdf

The core functions in this package: find_HDoutliers, display_HDoutliers

Full documentation and demos:

stray documentation built on July 2, 2020, 4:03 a.m.