  We propose a new method for clustering multivariate time-series data based on
Dynamic Linear Models. Whereas usual time-series clustering methods obtain
static membership parameters, our proposal allows each time-series to
dynamically change their cluster memberships over time. In this context, a
mixture model is assumed for the time series and a flexible Dirichlet evolution
for mixture weights allows for smooth membership changes over time. Posterior
estimates and predictions can be obtained through Gibbs sampling, but a more
efficient method for obtaining point estimates is presented, based on
Stochastic Expectation-Maximization and Gradient Descent. Finally, two
applications illustrate the usefulness of our proposed model to model both
univariate and multivariate time-series: World Bank indicators for the
renewable energy consumption of EU nations and the famous Gapminder dataset
containing life-expectancy and GDP per capita for various countries.
