Non-parametric smoothing and prediction for nonlinear circular time series

Not much research has been done in the field of circular time-series analysis. We propose a non-parametric theory for smoothing and prediction in the time domain for circular time-series data. Our model is based on local constant and local linear fitting estimates of a minimizer of an angular risk function. Both asymptotic arguments and empirical examples are used to describe the accuracy of our methods.