  Analyzing multivariate time series data is important to predict future events
and changes of complex systems in finance, manufacturing, and administrative
decisions. The expressiveness power of Gaussian Process (GP) regression methods
has been significantly improved by compositional covariance structures. In this
paper, we present a new GP model which naturally handles multiple time series
by placing an Indian Buffet Process (IBP) prior on the presence of shared
kernels. Our selective covariance structure decomposition allows exploiting
shared parameters over a set of multiple, selected time series. We also
investigate the well-definedness of the models when infinite latent components
are introduced. We present a pragmatic search algorithm which explores a larger
structure space efficiently. Experiments conducted on five real-world data sets
demonstrate that our new model outperforms existing methods in term of
structure discoveries and predictive performances.
