Journal article

Novel semi-metrics for multivariate change point analysis and anomaly detection

Nick James, Max Menzies, Lamiae Azizi, Jennifer Chan

PHYSICA D-NONLINEAR PHENOMENA | ELSEVIER | Published : 2020

Abstract

This paper proposes a new method for determining similarity and anomalies between time series, most practically effective in large collections of (likely related) time series, by measuring distances between structural breaks within such a collection. We introduce a class of semi-metric distance measures, which we term MJ distances. These semi-metrics provide an advantage over existing options such as the Hausdorff and Wasserstein metrics. We prove they have desirable properties, including better sensitivity to outliers, while experiments on simulated data demonstrate that they uncover similarity within collections of time series more effectively. Semi-metrics carry a potential disadvantage: ..

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University of Melbourne Researchers