GENERALIZED EXPONENTIAL SMOOTHING IN PREDICTION OF HIERARCHICAL TIME SERIES
| Autor: | Jerzy P. Rydlewski, Malgorzata Snarska, Dominik Mielczarek, Daniel Kosiorowski |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
FOS: Computer and information sciences depth for functional data ddc:519 Series (mathematics) Statistics & Probability Exponential smoothing Statistics - Applications Statistics - Computation hierarchical time series Methodology (stat.ME) forecast reconciliation Applied mathematics Applications (stat.AP) Statistics Probability and Uncertainty Time series functional time series lcsh:Statistics lcsh:HA1-4737 Computation (stat.CO) Statistics - Methodology Mathematics |
| Zdroj: | Statistics in Transition, Vol 19, Iss 2 (2018) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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