  Shang and Hyndman (2017) proposed a grouped functional time series
forecasting approach as a combination of individual forecasts obtained using
generalized least squares method. We modify their methodology using generalized
exponential smoothing technique for the most disaggregated functional time
series in order to obtain more robust predictor. We discuss some properties of
our proposals basing on results obtained via simulation studies and analysis of
real data related to a prediction of a demand for electricity in Australia in
2016.
