  Circular time series has received relatively little attention in statistics
and modeling complex circular time series using the state space approach is
non-existent in the literature. In this article we introduce a flexible
Bayesian nonparametric approach to state space modeling of observed circular
time series where even the latent states are circular random variables.
Crucially, we assume that the forms of both observational and evolutionary
functions, both of which are circular in nature, are unknown and time-varying.
We model these unknown circular functions by appropriate wrapped Gaussian
processes having desirable properties.
  We develop an effective Markov chain Monte Carlo strategy for implementing
our Bayesian model, by judiciously combining Gibbs sampling and
Metropolis-Hastings methods. Validation of our ideas with a simulation study
and two real bivariate circular time series data sets, where we assume one of
the variables to be unobserved, revealed very encouraging performance of our
model and methods.
  We finally analyse a data consisting of directions of whale migration,
considering the unobserved ocean current direction as the latent circular
process of interest. The results that we obtain are encouraging, and the
posterior predictive distribution of the observed process correctly predicts
the observed whale movement.
