Marcin Michalak, Silesian University of Technology, Poland

In the paper the analysis of the periodic kernels parameters is described. Periodic kernels can be used for the prediction task, performed as the typical regression problem. On the basis of the Periodic Kernel Estimator (PerKE) the prediction of real time series is performed. As periodic kernels require the setting of their parameters it is necessary to analyse their influence on the prediction quality. This paper describes an easy methodology of finding values of parameters of periodic kernels. It is based on grid search. Two different error measures are taken into consideration as the prediction qualities but lead to comparable results. The methodology was tested on benchmark and real datasets and proved to give satisfactory results.

Kernel regression, time series prediction, nonparametric regression