title: Multivariate Voronoi Outlier Detection for Time Series.
abstract: Outlier detection is a primary step in many data mining and analysis applications, including healthcare and medical research. This paper presents a general method to identify outliers in multivariate time series based on a Voronoi diagram, which we call Multivariate Voronoi Outlier Detection (MVOD). The approach copes with outliers in a multivariate framework, via designing and extracting effective attributes or features from the data that can take parametric or nonparametric forms. Voronoi diagrams allow for automatic configuration of the neighborhood relationship of the data points, which facilitates the differentiation of outliers and non-outliers. Experimental evaluation demonstrates that our MVOD is an accurate, sensitive, and robust method for detecting outliers in multivariate time series data.
authors.affiliation: Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA.
authors.forename: Chris E
authors.initials: CE
authors.lastname: Zwilling
authors.affiliation: Departments of Statistics, Psychology and Bioengineering, and the Beckman Institute, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA.
authors.forename: Michelle Yongmei
authors.initials: MY
authors.lastname: Wang