Circular data arise in many areas of application. Recently, there has been interest in looking
at circular data collected separately over time and over space. Here, we extend some of this
work to the spatio-temporal setting, introducing space–time dependence. We accommodate
covariates, implement full kriging and forecasting, and also allow for a nugget which can be
time dependent. We work within a Bayesian framework, introducing suitable latent variables
to facilitate Markov chain Monte Carlo model fitting. The Bayesian framework enables us to …