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  • Petropoulos 2018 judgmental-selection-forecasting

    Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Operations Management. 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 Journal of Operations Management, 60, 2018 DOI: 10.1016/J.JOM.2018.05.005

    Accepted author manuscript, 938 KB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

  • Petropoulos 2018 Judgmental selection of forecasting models

    Final published version, 1.06 MB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

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Judgmental Selection of Forecasting Models

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>2018
<mark>Journal</mark>Journal of Operations Management
Volume60
Number of pages13
Pages (from-to)34-46
Publication StatusPublished
Early online date18/06/18
<mark>Original language</mark>English

Abstract

In this paper, we explored how judgment can be used to improve the selection of a forecasting model. We compared the performance of judgmental model selection against a standard algorithm based on information criteria. We also examined the efficacy of a judgmental model-build approach, in which experts were asked to decide on the existence of the structural components (trend and seasonality) of the time series instead of directly selecting a model from a choice set. Our behavioral study used data from almost 700 participants, including forecasting practitioners. The results from our experiment suggest that selecting models judgmentally results in performance that is on par, if not better, to that of algorithmic selection. Further, judgmental model selection helps to avoid the worst models more frequently compared to algorithmic selection. Finally, a simple combination of the statistical and judgmental selections and judgmental aggregation significantly outperform both statistical and judgmental selections.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Journal of Operations Management. 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 Journal of Operations Management, 60, 2018 DOI: 10.1016/J.JOM.2018.05.005