Stochastic coherency in forecast reconciliation

Pritularga, Kandrika and Svetunkov, Ivan and Kourentzes, Nikolaos (2021) Stochastic coherency in forecast reconciliation. International Journal of Production Economics, 240: 108221. ISSN 0925-5273

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Hierarchical forecasting has been receiving increasing attention in the literature. The notion of coherency is central to this, which implies that the hierarchical time series follows some linear aggregation constraints. This notion, however, does not take several modelling uncertainties into account. We propose to redefine coherency as stochastic. This allows to accommodate overlooked uncertainties in forecast reconciliation. We show analytically that there are two potential sources of uncertainty in forecast reconciliation. We use simulated data to demonstrate how these uncertainties propagate to the covariance matrix estimation, introducing uncertainty in the reconciliation weights matrix. This then increases the uncertainty of the reconciled forecasts. We apply our understanding to modelling accident and emergency admissions in a UK hospital. Our analysis confirms the insights from stochastic coherency in forecast reconciliation. It shows that we gain accuracy improvement from forecast reconciliation, on average, at the cost of the variability of the forecast error distribution. Users may opt to prefer less volatile error distributions to assist decision making.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Production Economics
Additional Information:
This is the author’s version of a work that was accepted for publication in International Journal of Production Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Production Economics, 240, 2021 DOI: 10.1016/j.ijpe.2021.108221
Uncontrolled Keywords:
?? forecastingcoherencymodel uncertaintyforecast combinationcovariance estimationbusiness, management and accounting(all)economics and econometricsmanagement science and operations researchindustrial and manufacturing engineering ??
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Deposited On:
13 Sep 2021 16:15
Last Modified:
10 Jan 2024 00:31