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Two multivariate online change detection models.

Lingzhe Guo1, Reza Modarres1

  • 1Department of Statistics, The George Washington University, Washington, DC, USA.

Journal of Applied Statistics
|June 16, 2022
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Summary
This summary is machine-generated.

This study introduces two non-parametric online change detection methods using energy statistics and Mahalanobis depth. These methods effectively monitor data streams for distribution changes, as demonstrated by their application to river flow data.

Keywords:
62G1062G2062H15Online change detectiondepth modelenergy statisticsnonparametricsliding-window algorithm

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Area of Science:

  • Data Science
  • Statistical Monitoring
  • Environmental Science

Background:

  • Online change point detection is crucial for monitoring data streams.
  • Existing methods require efficient and robust algorithms for real-time analysis.
  • Non-parametric methods offer flexibility in detecting distribution changes.

Purpose of the Study:

  • To introduce and evaluate two novel non-parametric online change detection methods.
  • To compare their performance against existing techniques for detecting changes in data streams.
  • To assess the applicability of these methods in environmental monitoring, specifically river flow volume.

Main Methods:

  • Development of a sliding-window algorithm for energy statistics-based change detection.
  • Proposal of a threshold training algorithm for Mahalanobis depth-based change detection, ensuring control over false alarms.
  • Numerical simulations to compare the proposed methods with three existing online change point detection techniques.

Main Results:

  • The proposed energy statistics and Mahalanobis depth methods demonstrate effective detection of changes in data stream mean and variability.
  • Performance evaluation indicates competitive or superior results compared to existing methods in numerical studies.
  • Successful application to real-world environmental data, specifically monitoring changes in the Mississippi River's flowing volume.

Conclusions:

  • The presented non-parametric online change detection methods offer a robust and efficient approach for monitoring data streams.
  • These methods are valuable tools for both statistical analysis and practical environmental monitoring applications.
  • The Mahalanobis depth method provides a controllable way to manage false alarms in change point detection.