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Two multivariate online change detection models

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  • Lingzhe Guo
  • Reza Modarres

Abstract

Online change point detection methods monitor changes in the distribution of a data stream. This article discusses two non-parametric online change detection methods based on the energy statistics and Mahalanobis depth. To apply the energy statistic, we use sliding-window algorithm with efficient training and updating procedures. For Mahalanobis depth, we propose an algorithm to train the threshold with desired protective ability against false alarms and discuss factors that have an influence on the threshold. Numerical studies evaluate and compare the performance of the proposed models with three existing methods to detect changes in the mean and variability of a data stream. The methods are applied to detecting changes in the flowing volume of the Mississippi River.

Suggested Citation

  • Lingzhe Guo & Reza Modarres, 2022. "Two multivariate online change detection models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(2), pages 427-448, January.
    • Handle: RePEc:taf:japsta:v:49:y:2022:i:2:p:427-448
    DOI: 10.1080/02664763.2020.1815674
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