  A dynamic factor model with a mixture distribution of the loadings is
introduced and studied for multivariate, possibly high-dimensional time series.
The correlation matrix of the model exhibits a block structure, reminiscent of
correlation patterns for many real multivariate time series. A standard
$k$-means algorithm on the loadings estimated through principal components is
used to cluster component time series into communities with accompanying bounds
on the misclustering rate. This is one standard method of community detection
applied to correlation matrices viewed as weighted networks. This work puts a
mixture model, a dynamic factor model and network community detection in one
interconnected framework. Performance of the proposed methodology is
illustrated on simulated and real data.
