Dynamic clustering of time series data
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.
ArXiv