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A model for the prediction of functional time series is introduced, whereobservations are assumed to be continuous random functions. We model thedependence of the data with a nonstandard autoregressive structure, motivatedin terms of the Reproducing Kernel Hilbert Space (RKHS) generated by theauto-covariance function of the data. The new approach helps to find relevantpoints of the curves in terms of prediction accuracy. This dimension reductiontechnique is particularly useful for applications, since the results areusually directly interpretable in terms of the original curves. An empiricalstudy involving real and simulated data is included, which generatescompetitive results. Supplementary material includes R-Code, tables andmathematical comments.

Variable selection for the prediction of C[0,1]-valued AR processes using RKHS