Time-based detection of changes to multivariate patterns

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 probabilities ratios are derived from a relationship to likelihood ratios to form the basis for time-weighted updates of the monitoring scheme.

• Type: Journal
• Year: 2010
• Where: ANOR
• Authors: Jing Hu, George C. Runger
• Keywords: ANOR 2010, Class Probabilities Ratios, Complex In-control Regions, Individual Observation Vector