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Generalized Exponential Smoothing In Prediction Of Hierarchical Time Series

Author

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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 .)

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.

Suggested Citation

  • Kosiorowski Daniel & Mielczarek Dominik & Rydlewski Jerzy P. & Snarska Małgorzata, 2018. "Generalized Exponential Smoothing In Prediction Of Hierarchical Time Series," Statistics in Transition New Series, Polish Statistical Association, vol. 19(2), pages 331-350, June.
    • Handle: RePEc:vrs:stintr:v:19:y:2018:i:2:p:331-350:n:8
    DOI: 10.21307/stattrans-2018-019
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    References listed on IDEAS

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