GENERALIZED EXPONENTIAL SMOOTHING IN PREDICTION OF HIERARCHICAL TIME SERIES
GENERALIZED EXPONENTIAL SMOOTHING IN PREDICTION OF HIERARCHICAL TIME SERIES
Author(s): Daniel Kosiorowski, Dominik Mielczarek, Jerzy Rydlewski, Małgorzata SnarskaSubject(s): Economy, National Economy, Public Finances
Published by: Główny Urząd Statystyczny
Keywords: functional time series; hierarchical time series; forecast reconciliation; depth for functional data
Summary/Abstract: Shang and Hyndman (2017) proposed a grouped functional time series forecasting approach as a combination of individual forecasts obtained using the generalized least squares method. We modify their methodology using a generalized exponential smoothing technique for the most disaggregated functional time series in order to obtain a more robust predictor. We discuss some properties of our proposals based on the results obtained via simulation studies and analysis of real data related to the prediction of demand for electricity in Australia in 2016.
Journal: Statistics in Transition. New Series
- Issue Year: 19/2018
- Issue No: 2
- Page Range: 331-350
- Page Count: 20
- Language: English