Generalized  exponential smoothing in prediction of hierarchical time series

Daniel  Kosiorowski; Dominik  Mielczarek; Jerzy P.  Rydlewski; Małgorzata  Snarska

doi:https://doi.org/10.21307/stattrans-2018-019

Generalized exponential smoothing in prediction of hierarchical time series

Daniel Kosiorowski Department of Statistics, Cracow University of Economics, Krakow, Poland. ´ , Dominik Mielczarek AGH University of Science and Technology, Faculty of Applied Mathematics, al. A. Mickiewicza 30, 30-059 Krakow, Poland. , Jerzy P. Rydlewski AGH University of Science and Technology, Faculty of Applied Mathematics, al. A. Mickiewicza 30, 30-059 Krakow, Poland. , Małgorzata Snarska Department of Financial Markets, Cracow University of Economics, Krakow, Poland. Statistics in Transition new series, vol. 19, 2018, 2, pages: 331-350 Published online: 1 June 2018 https://doi.org/10.21307/stattrans-2018-019

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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

KEYWORDS

functional time series, hierarchical time series, forecast reconciliation, depth for functional data.

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