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On the evaluation of hierarchical forecasts
Department of Econometrics and Business Statistics, Monash University, Australia.
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-0211-5218
2023 (English)In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 39, no 4, p. 1502-1511Article in journal (Refereed) Published
Abstract [en]

The aim of this paper is to provide a thinking road-map and a practical guide to researchers and practitioners working on hierarchical forecasting problems. Evaluating the performance of hierarchical forecasts comes with new challenges stemming from both the structure of the hierarchy and the application context. We discuss several relevant dimensions for researchers and analysts: the scale and units of the time series, the issue of intermittency, the forecast horizon, the importance of multiple evaluation windows and the multiple objective decision context. We conclude with a series of practical recommendations. 

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 39, no 4, p. 1502-1511
Keywords [en]
Aggregation, Coherence, Hierarchical time series, Multiple objectives, Reconciliation
National Category
Probability Theory and Statistics Computer Sciences Computer Systems
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-22015DOI: 10.1016/j.ijforecast.2022.08.003ISI: 001072979600001Scopus ID: 2-s2.0-85140264407OAI: oai:DiVA.org:his-22015DiVA, id: diva2:1708179
Note

© 2022 International Institute of Forecasters

Available from: 2022-11-03 Created: 2022-11-03 Last updated: 2023-10-13Bibliographically approved

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Kourentzes, Nikolaos

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