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Optimally Reconciling Forecasts in a Hierarchy

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  • Rob J. Hyndman
  • George Athanasopoulos

Abstract

We know that when forecasting the product hierarchy, reconciliation is invariably needed to make the sum of lower-level forecasts equate to the upperlevel forecasts. The authors argue that the traditional Bottom-Up, Top-Down, and Middle-Out procedures for reconciliation all fail to make best use of the available data. They show we can do better by taking weighted averages of the forecasts from different levels, an approach they call optimal reconciliation. Copyright International Institute of Forecasters, 2014

Suggested Citation

  • Rob J. Hyndman & George Athanasopoulos, 2014. "Optimally Reconciling Forecasts in a Hierarchy," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 35, pages 42-48, Fall.
    • Handle: RePEc:for:ijafaa:y:2014:i:35:p:42-48
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    Cited by:

    1. Suominen, Arho & Toivanen, Hannes & Seppänen, Marko, 2017. "Firms' knowledge profiles: Mapping patent data with unsupervised learning," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 131-142.
    2. Nasios, Ioannis & Vogklis, Konstantinos, 2022. "Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1448-1459.
    3. Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "Evaluating quantile forecasts in the M5 uncertainty competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1531-1545.
    4. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    5. Li, Han & Li, Hong & Lu, Yang & Panagiotelis, Anastasios, 2019. "A forecast reconciliation approach to cause-of-death mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 122-133.
    6. Li Bai & Pierre Pinson, 2019. "Distributed Reconciliation in Day-Ahead Wind Power Forecasting," Energies, MDPI, vol. 12(6), pages 1-19, March.
    7. George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Anastasios Panagiotelis, 2023. "Forecast Reconciliation: A Review," Monash Econometrics and Business Statistics Working Papers 8/23, Monash University, Department of Econometrics and Business Statistics.
    8. Hong, Tao & Xie, Jingrui & Black, Jonathan, 2019. "Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1389-1399.

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