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

TL;DR

This paper introduces MJ semi-metrics for measuring similarity and detecting anomalies in large collections of multivariate time series, outperforming existing metrics in sensitivity and effectiveness.

Contribution

The paper proposes a new class of semi-metrics called MJ distances, with proven properties and demonstrated advantages over traditional metrics like Hausdorff and Wasserstein.

Findings

MJ distances are more sensitive to outliers.

Experiments show improved similarity detection in time series collections.

Application to real data demonstrates practical utility.

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 \emph{semi-metric} distance measures, which we term \emph{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: without the triangle inequality, they may not satisfy a "transitivity property of closeness." We analyse this failure with proof and introduce an computational method to investigate, in which we demonstrate that our semi-metrics violate transitivity infrequently and mildly. Finally, we apply our methods to cryptocurrency and measles data, introducing a judicious application of eigenvalue analysis.