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Time-based detection of changes to multivariate patterns

Author

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  • Jing Hu
  • George Runger

Abstract

Detection of changes to multivariate patterns is an important topic in a number of different domains. Modern data sets often include categorical and numerical data and potentially complex in-control regions. Given a flexible, robust decision rule for this environment that signals based on an individual observation vector, an important issue is how to extend the rule to incorporate time-based information. A decision rule can be learned to detect shifts through artificial data that transforms the problem to one of supervised learning. Then class probability ratios are derived from a relationship to likelihood ratios to form the basis for time-weighted updates of the monitoring scheme. Copyright Springer Science+Business Media, LLC 2010

Suggested Citation

  • Jing Hu & George Runger, 2010. "Time-based detection of changes to multivariate patterns," Annals of Operations Research, Springer, vol. 174(1), pages 67-81, February.
    • Handle: RePEc:spr:annopr:v:174:y:2010:i:1:p:67-81:10.1007/s10479-009-0610-8
    DOI: 10.1007/s10479-009-0610-8
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    Cited by:

    1. Yu-min Liu & Li Xue, 2015. "The optimization design of EWMA charts for monitoring environmental performance," Annals of Operations Research, Springer, vol. 228(1), pages 113-124, May.

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