Fast computation of reconciled forecasts for hierarchical and grouped time series

Rob Hyndman, Alan Lee () and Earo Wang ()

No 17/14, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: We describe some fast algorithms for reconciling large collections of time series forecasts with aggregation constraints. The constraints arise due to the need for forecasts of collections of time series with hierarchical or grouped structures to add up in the same manner as the observed time series. We show that the least squares approach to reconciling hierarchical forecasts can be extended to more general non-hierarchical groups of time series, and that the computations can be handled efficiently by exploiting the structure of the associated design matrix. Our algorithms will reconcile hierarchical forecasts with hierarchies of unlimited size, making forecast reconciliation feasible in business applications involving very large numbers of time series.

Keywords: combining forecasts; grouped time series; hierarchical time series; reconciling forecasts; weighted least squares. (search for similar items in EconPapers)
JEL-codes: C32 C53 C55 C63 (search for similar items in EconPapers)
Pages: 26
Date: 2014
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Fast computation of reconciled forecasts for hierarchical and grouped time series (2016)
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