Bayesian Mixture of AR Models for Time Series Clustering

In this paper we propose a Bayesian framework for estimation of parameters of a mixture of autoregressive model, for time series clustering. The proposed approach is based on variational principles and provides a tractable approximation to the true posterior density that minimizes Kullback-Liebler(KL) divergence w.r.t prior distribution. The proposed approach is applied both on simulated and real time series data sets and found to be useful in exploring and finding the true number of underlying clusters, starting from arbitrarily large number clusters.