GENERALIZED EXPONENTIAL SMOOTHING IN PREDICTION OF HIERARCHICAL TIME SERIES

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Statistics in Transition New Series

Polish Statistical Association

Central Statistical Office of Poland

Subject: Economics , Statistics & Probability

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VOLUME 19 , ISSUE 2 (June 2018) > List of articles

GENERALIZED EXPONENTIAL SMOOTHING IN PREDICTION OF HIERARCHICAL TIME SERIES

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

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

Citation Information : Statistics in Transition New Series. Volume 19, Issue 2, Pages 331-350, DOI: https://doi.org/10.21307/stattrans-2018-019

License : (CC BY-NC-ND 4.0)

Published Online: 28-July-2018

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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.

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