Judgmental Selection of Forecasting Models (Reprint)

Fotios Petropoulos, Nikolaos Kourentzes, Konstantinos Nikolopoulos, Enno Siemsen

Research output: Chapter or section in a book/report/conference proceedingChapter or section

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.

Original languageEnglish
Title of host publicationJudgment in Predictive Analytics
EditorsM. Seifert
Place of PublicationCham, Switzerland
PublisherSpringer
Pages53-84
Number of pages32
ISBN (Electronic)9783031300851
ISBN (Print)9783031300844
DOIs
Publication statusPublished - 3 Jun 2023

Publication series

NameInternational Series in Operations Research and Management Science
Volume343
ISSN (Print)0884-8289
ISSN (Electronic)2214-7934

Bibliographical note

Funding Information:
Acknowledgments FP and NK would like to acknowledge the support for conducting this research provided by the Lancaster University Management School Early Career Research Grant MTA7690.

Keywords

  • Behavioral operations
  • Combination
  • Decomposition
  • Judgmental forecasting
  • Model selection

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Strategy and Management
  • Management Science and Operations Research
  • Applied Mathematics

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