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Sequential Nonparametric Algorithm for Detecting Time Series Breakdown

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Abstract

We consider the problem of detecting spontaneous changes in the characteristics of a time series (a time series breakdown), when the breakdown manifests itself in the form of a stepwise change in the value of the location parameter (expectation or median) of the probability distribution function of the controlled process. It is proposed to solve this problem in real time applying a sequential nonparametric detection algorithm based on using the random walk theory mechanism. By computer simulation we investigate the probabilistic characteristics of the proposed algorithm and its efficiency in comparison with the well-known CUSUM-algorithm of parametric type. A procedure for synthesizing a control algorithm with the specified properties is given. It emphasizes the prospects of using such an algorithm in monitoring systems for various purposes, usually created in conditions of shortage of a priori information about the properties of the controlled process.

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Authors and Affiliations

  1. National Medical Research Center of Children’s Hematology, Oncology, and Immunology Named after Dmitry Rogachev, Moscow, 117198, Russia

    A. A. Chervova

  2. National Research University “Moscow Power Engineering Institute”, Moscow, 111250, Russia

    G. F. Filaretov &  Bouchaala Zineddine

Authors
  1. A. A. Chervova
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  2. G. F. Filaretov
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  3. Bouchaala Zineddine
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Corresponding authors

Correspondence to A. A. Chervova, G. F. Filaretov or Bouchaala Zineddine.

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Translated by V. Potapchouck

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Chervova, A.A., Filaretov, G.F. & Bouchaala Zineddine Sequential Nonparametric Algorithm for Detecting Time Series Breakdown. Autom Remote Control 82, 2204–2212 (2021). https://doi.org/10.1134/S0005117921120110

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  • DOI: https://doi.org/10.1134/S0005117921120110

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