Model-based Clustering of Time Series - A Review from a Bayesian Perspective

Clustering is a widely used statistical tool to determine subsets in a given data set. Frequently used clustering methods are mostly based on distance measures and cannot easily be extended to cluster time series within a panel or a longitudinal data set. The paper reviews recently suggested approaches to model-based clustering of panel or longitudinal data based on finite mixture models. Several approaches are considered that are suitable both for continuous as well as categorical time series observations. Bayesian estimation through Markov chain Monte Carlo methods is described in detail and various criteria to select the number of clusters are reviewed. An application to a panel of marijuana use among teenagers serves as an illustration.