Título: Dynamic Clustering of Time Series Data.

Data: 04/05/2020
Horário: 10h00

Resumo: 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.

Banca Examinadora:

Thaís C. O. Fonseca (Presidente)
Guilherme Ost (DME, UFRJ)
Daniel Ratton Figueiredo (Pesc, UFRJ)
Marina Paez (Suplente)