  An approach is presented for making predictions about functional time series.
The method is applied to data coming from periodically correlated processes and
electricity demand, obtaining accurate point forecasts and narrow prediction
bands that cover high proportions of the forecasted functional datum, for a
given confidence level. The method is computationally efficient and
substantially different to other functional time series methods, offering a new
insight for the analysis of these data structures.
