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Optimal combination forecasts for hierarchical time series

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Author Info
Rob J. Hyndman ()
Roman A. Ahmed ()
George Athanasopoulos ()

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Abstract

In many applications, there are multiple time series that are hierarchically organized and can be aggregated at several different levels in groups based on products, geography or some other features. We call these "hierarchical time series". They are commonly forecast using either a "bottom-up" or a "top-down" method. In this paper we propose a new approach to hierarchical forecasting which provides optimal forecasts that are better than forecasts produced by either a top-down or a bottom-up approach. Our method is based on independently forecasting all series at all levels of the hierarchy and then using a regression model to optimally combine and reconcile these forecasts. The resulting revised forecasts add up appropriately across the hierarchy, are unbiased and have minimum variance amongst all combination forecasts under some simple assumptions. We show in a simulation study that our method performs well compared to the top-down approach and the bottom-up method. It also allows us to construct prediction intervals for the resultant forecasts. Finally, we apply the method to forecasting Australian tourism demand where the data are disaggregated by purpose of visit and geographical region.

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File URL: http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/2007/wp9-07.pdf
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Publisher Info
Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 9/07.

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Length: 22 pages
Date of creation: Jul 2007
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Handle: RePEc:msh:ebswps:2007-9

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Related research
Keywords: Bottom-up forecasting combining forecasts GLS regression hierarchical forecasting Moore-Penrose inverse reconciling forecasts top-down forecasting.

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Find related papers by JEL classification:
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models
C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Kohn, Robert, 1982. "When is an aggregate of a time series efficiently forecast by its past?," Journal of Econometrics, Elsevier, vol. 18(3), pages 337-349, April. [Downloadable!] (restricted)
  2. Tobias, Justin & Zellner, Arnold, 2004. "A Note on Aggregation, Disaggregation and Forecasting Performance," Staff General Research Papers 12024, Iowa State University, Department of Economics.
    Other versions:
  3. Dangerfield, Byron J. & Morris, John S., 1992. "Top-down or bottom-up: Aggregate versus disaggregate extrapolations," International Journal of Forecasting, Elsevier, vol. 8(2), pages 233-241, October. [Downloadable!] (restricted)
  4. Edwards, John B & Orcutt, Guy H, 1969. "Should Aggregation Prior to Estimation Be the Rule?," The Review of Economics and Statistics, MIT Press, vol. 51(4), pages 409-20, November. [Downloadable!] (restricted)
  5. George Athanasopoulos & Roman A. Ahmed & Rob J. Hyndman, 2007. "Hierarchical forecasts for Australian domestic tourism," Monash Econometrics and Business Statistics Working Papers 12/07, Monash University, Department of Econometrics and Business Statistics, revised Nov 2007. [Downloadable!]
  6. Tiao, G. C. & Guttman, Irwin, 1980. "Forecasting contemporal aggregates of multiple time series," Journal of Econometrics, Elsevier, vol. 12(2), pages 219-230, February. [Downloadable!] (restricted)
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