Judgmental selection of forecasting models

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Fältnamn	Indikatorer	Metadata
000		03401naa a2200517 4500
001		oai:DiVA.org:his-18240
003		SwePub
008		200228s2018 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-182402 URI
024	7	a https://doi.org/10.1016/j.jom.2018.05.0052 DOI
040		a (SwePub)his
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a Petropoulos, Fotiosu School of Management, University of Bath, United Kingdom4 aut
245	1 0	a Judgmental selection of forecasting models
264		c 2018-06-18
264	1	b John Wiley & Sons,c 2018
338		a electronic2 rdacarrier
500		a CC BY 4.0
520		a 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.
650	7	a NATURVETENSKAPx Matematikx Sannolikhetsteori och statistik0 (SwePub)101062 hsv//swe
650	7	a NATURAL SCIENCESx Mathematicsx Probability Theory and Statistics0 (SwePub)101062 hsv//eng
653		a Behavioral operations
653		a Combination
653		a Decomposition
653		a Model selection
653		a Industrial engineering
653		a Operations research
653		a Behavioral studies
653		a Forecasting modeling
653		a Information criterion
653		a Standard algorithms
653		a Structural component
653		a Forecasting
700	1	a Kourentzes, Nikolaosu Lancaster University Management School, Lancaster University, United Kingdom4 aut0 (Swepub:his)koun
700	1	a Nikolopoulos, Konstantinosu Bangor Business School, Bangor University, United Kingdom4 aut
700	1	a Siemsen, Ennou Wisconsin School of Business, University of Wisconsin, USA4 aut
710	2	a School of Management, University of Bath, United Kingdomb Lancaster University Management School, Lancaster University, United Kingdom4 org
773	0	t Journal of Operations Managementd : John Wiley & Sonsg 60, s. 34-46q 60<34-46x 0272-6963x 1873-1317
856	4	u https://doi.org/10.1016/j.jom.2018.05.005y Fulltext
856	4	u https://his.diva-portal.org/smash/get/diva2:1400699/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
856	4	u https://onlinelibrary.wiley.com/doi/pdfdirect/10.1016/j.jom.2018.05.005
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18240
856	4 8	u https://doi.org/10.1016/j.jom.2018.05.005

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