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Judgmental selection of forecasting models
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- Petropoulos, Fotios (author)
- School of Management, University of Bath, United Kingdom
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- Kourentzes, Nikolaos (author)
- Lancaster University Management School, Lancaster University, United Kingdom
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- Nikolopoulos, Konstantinos (author)
- Bangor Business School, Bangor University, United Kingdom
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- (creator_code:org_t)
- 2018-06-18
- 2018
- English.
- In: Journal of Operations Management. - : John Wiley & Sons. - 0272-6963 .- 1873-1317. ; 60, s. 34-46
- Journal article (peer-reviewed)
Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Keyword
- Behavioral operations
- Combination
- Decomposition
- Model selection
- Industrial engineering
- Operations research
- Behavioral studies
- Forecasting modeling
- Information criterion
- Standard algorithms
- Structural component
- Forecasting
Publication and Content Type
- ref (subject category)
- art (subject category)
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- University of Skövde
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