Time-based detection of changes to multivariate patterns

Jing Hu () and George Runger

Annals of Operations Research, 2010, vol. 174, issue 1, 67-81

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

Keywords: Time-based detection of changes; Multivariate patterns; Supervised learning (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/s10479-009-0610-8

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