GENERALIZED EXPONENTIAL SMOOTHING IN PREDICTION OF HIERARCHICAL TIME SERIES

Kosiorowski Daniel, Mielczarek Dominik (), Rydlewski Jerzy P. () and Snarska Małgorzata ()
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Kosiorowski Daniel: Department of Statistics, Cracow University of Economics, Krakow, Poland .
Mielczarek Dominik: AGH University of Science and Technology, Faculty of Applied Mathematics, al. A. Mickiewicza 30, 30-059 Krakow, Poland .
Rydlewski Jerzy P.: AGH University of Science and Technology, Faculty of Applied Mathematics, al. A. Mickiewicza 30, 30-059 Krakow, Poland .
Snarska Małgorzata: Department of Financial Markets, Cracow University of Economics, Krakow, Poland .

Statistics in Transition New Series, 2018, vol. 19, issue 2, 331-350

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 orderto 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. (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:19:y:2018:i:2:p:331-350:n:8

DOI: 10.21307/stattrans-2018-019

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