Optimal weighting models based on linear uncertain constraints in intuitionistic fuzzy preference relations

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dc.cclicenceCC-BY-NCen
dc.contributor.authorGong, Zaiwuen
dc.contributor.authorTan, Xiaoen
dc.contributor.authorYang, Yingjieen
dc.date.acceptance2018-06-07en
dc.date.accessioned2019-01-15T10:13:31Z
dc.date.available2019-01-15T10:13:31Z
dc.date.issued2018-11-09
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractAlthough the classic exponential-smoothing models and grey prediction models have been widely used in time series forecasting, this paper shows that they are susceptible to fluctu- ations in samples. A new fractional bidirectional weakening buffer operator for time series prediction is proposed in this paper. This new operator can effectively reduce the negative impact of unavoidable sample fluctuations. It overcomes limitations of existing weakening buffer operators, and permits better control of fluctuations from the entire sample period. Due to its good performance in improving stability of the series smoothness, the new op- erator can better capture the real developing trend in raw data and improve forecast accu- racy. The paper then proposes a novel methodology that combines the new bidirectional weakening buffer operator and the classic grey prediction model. Through a number of case studies, this method is compared with several classic models, such as the exponential smoothing model and the autoregressive integrated moving average model, etc. Values of three error measures show that the new method outperforms other methods, especially when there are data fluctuations near the forecasting horizon. The relative advantages of the new method on small sample predictions are further investigated. Results demonstrate that model based on the proposed fractional bidirectional weakening buffer operator has higher forecasting accuracy.en
dc.funderLeverhulmeen
dc.funderRoyal Societyen
dc.identifier.citationGong, Z., Tan, X. and Yang, Y. (2019) Optimal weighting models based on linear uncertain constraints in intuitionistic fuzzy preference relations. Journal of the Operational Research Society,en
dc.identifier.doihttps://doi.org/10.1080/01605682.2018.1489349
dc.identifier.issn1476-9360
dc.identifier.urihttp://hdl.handle.net/2086/17432
dc.language.isoenen
dc.peerreviewedYesen
dc.projectid2014-IN-020en
dc.projectidIEC\NSFC\170391en
dc.publisherTaylor and Francisen
dc.researchgroupInstitute of Artificial Intelligence (IAI)en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectgroup decision makingen
dc.subjectintuitionistic fuzzy preference relationen
dc.subjectchance constrainten
dc.subjectuncertainty distributionen
dc.subjectadditive consistencyen
dc.subjectuncertain programmingen
dc.titleOptimal weighting models based on linear uncertain constraints in intuitionistic fuzzy preference relationsen
dc.typeArticleen

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