Journal article

Anomaly Detection in Streaming Nonstationary Temporal Data

Priyanga Dilini Talagala, Rob J Hyndman, Kate Smith-Miles, Sevvandi Kandanaarachchi, Mario A Munoz

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS | AMER STATISTICAL ASSOC | Published : 2019

Abstract

This article proposes a framework that provides early detection of anomalous series within a large collection of nonstationary streaming time-series data. We define an anomaly as an observation, that is, very unlikely given the recent distribution of a given system. The proposed framework first calculates a boundary for the system’s typical behavior using extreme value theory. Then a sliding window is used to test for anomalous series within a newly arrived collection of series. The model uses time series features as inputs, and a density-based comparison to detect any significant changes in the distribution of the features. Using various synthetic and real world datasets, we demonstrate the..

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Grants

Awarded by Australian Research Council through the Linkage Project


Funding Acknowledgements

This research was supported in part by the Monash eResearch Centre and eSolutions-Research Support Services through the use of the MonARCH (Monash Advanced Research Computing Hybrid) HPC Cluster. Funding was provided by the Australian Research Council through the Linkage Project LP160101885.