Although most circular datasets are in the form of time series, not much research has been done in the field of circular time series analysis. We propose a nonparametric theory for smoothing and prediction in the time domain for circular time series data. Our model is based on local polynomial fitting which minimizes an angular risk function. Both asymptotic arguments and empirical examples are used to describe the accuracy of our methods.
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