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Bayesian mixture of AR models for time series clustering

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Abstract

In this paper, we propose a Bayesian framework for estimation of parameters of a mixture of autoregressive models 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 with respect to prior distribution. This method simultaneously addresses the model complexity and parameter estimation problems. The proposed approach is applied both on simulated and real-world time series datasets. It is found to be useful in exploring and finding the true number of underlying clusters, starting from an arbitrarily large number of clusters.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, 600036, India

    B. Venkataramana Kini & C. Chandra Sekhar

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  1. B. Venkataramana Kini
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  2. C. Chandra Sekhar
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Correspondence to B. Venkataramana Kini.

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Venkataramana Kini, B., Chandra Sekhar, C. Bayesian mixture of AR models for time series clustering. Pattern Anal Applic 16, 179–200 (2013). https://doi.org/10.1007/s10044-011-0247-5

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