  In this paper, we consider the task of clustering a set of individual time
series while modeling each cluster, that is, model-based time series
clustering. The task requires a parametric model with sufficient flexibility to
describe the dynamics in various time series. To address this problem, we
propose a novel model-based time series clustering method with mixtures of
linear Gaussian state space models, which have high flexibility. The proposed
method uses a new expectation-maximization algorithm for the mixture model to
estimate the model parameters, and determines the number of clusters using the
Bayesian information criterion. Experiments on a simulated dataset demonstrate
the effectiveness of the method in clustering, parameter estimation, and model
selection. The method is applied to real datasets commonly used to evaluate
time series clustering methods. Results showed that the proposed method
produces clustering results that are as accurate or more accurate than those
obtained using previous methods.
