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.