Change Detection in Multivariate Datastreams Controlling False Alarms

We introduce QuantTree Exponentially Weighted Moving Average (QT-EWMA), a novel change-detection algorithm for multivariate datastreams that can operate in a nonparametric and online manner. QT-EWMA can be configured to yield a target Average Run Length (ARL0 $$_0$$ ), thus controlling the expected...

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Bibliographic Details
Published inMachine Learning and Knowledge Discovery in Databases. Research Track Vol. 12975; pp. 421 - 436
Main Authors Frittoli, Luca, Carrera, Diego, Boracchi, Giacomo
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2021
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
False alarms
Histograms
Multivariate datastreams
Nonparametric monitoring
Online change detection
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Summary:We introduce QuantTree Exponentially Weighted Moving Average (QT-EWMA), a novel change-detection algorithm for multivariate datastreams that can operate in a nonparametric and online manner. QT-EWMA can be configured to yield a target Average Run Length (ARL0 $$_0$$ ), thus controlling the expected time before a false alarm. Control over false alarms has many practical implications and is rarely guaranteed by online change-detection algorithms that can monitor multivariate datastreams whose distribution is unknown. Our experiments, performed on synthetic and real-world datasets, demonstrate that QT-EWMA controls the ARL0 $$_0$$ and the false alarm rate better than state-of-the-art methods operating in similar conditions, achieving comparable detection delays.
Bibliography:Electronic supplementary materialThe online version of this chapter (https://doi.org/10.1007/978-3-030-86486-6_26) contains supplementary material, which is available to authorized users.
Original Abstract: We introduce QuantTree Exponentially Weighted Moving Average (QT-EWMA), a novel change-detection algorithm for multivariate datastreams that can operate in a nonparametric and online manner. QT-EWMA can be configured to yield a target Average Run Length (ARL0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_0$$\end{document}), thus controlling the expected time before a false alarm. Control over false alarms has many practical implications and is rarely guaranteed by online change-detection algorithms that can monitor multivariate datastreams whose distribution is unknown. Our experiments, performed on synthetic and real-world datasets, demonstrate that QT-EWMA controls the ARL0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_0$$\end{document} and the false alarm rate better than state-of-the-art methods operating in similar conditions, achieving comparable detection delays.
ISBN:3030864855
9783030864859
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-86486-6_26