  Motivated by an increasing demand for models that can effectively describe
features of complex multivariate time series, e.g. from sensor data in
biomechanics, motion analysis, and sports science, we introduce a novel
state-space modeling framework where the state equation encodes the evolution
of latent partitions of the data over time. Building on the principles of
dynamic linear models, our approach develops a random partition model capable
of linking data partitions to previous ones over time, using a straightforward
Markov structure that accounts for temporal persistence and facilitates
changepoint detection. The selection of changepoints involves multiple
dependent decisions, and we address this time-dependence by adopting a
non-marginal false discovery rate control. This leads to a simple decision rule
that ensures more stringent control of the false discovery rate compared to
approaches that do not consider dependence. The method is efficiently
implemented using a Gibbs sampling algorithm, leading to a straightforward
approach compared to existing methods for dependent random partition models.
Additionally, we show how the proposed method can be adapted to handle
multi-view clustering scenarios. Simulation studies and the analysis of a human
gesture phase dataset collected through various sensing technologies show the
effectiveness of the method in clustering multivariate time series and
detecting changepoints.
