Show simple item record

dc.contributor.authorWu, L.en
dc.contributor.authorLiu, S.en
dc.contributor.authorYang, Yingjieen
dc.contributor.authorMa, L.en
dc.contributor.authorLiu, H.en
dc.date.accessioned2016-04-18T14:16:26Z
dc.date.available2016-04-18T14:16:26Z
dc.date.issued2016-04-20
dc.identifier.citationWu, L., Liu, S., Yang, Y., Ma, L., Liu, H. (2016) Multi-variable weakening buffer operator and its application. Information Sciences, 339, pp. 98-107en
dc.identifier.issn0020-0255
dc.identifier.issn1872-6291
dc.identifier.urihttp://hdl.handle.net/2086/11912
dc.description.abstractTo weaken the disturbances of multi-variable and reveal the real situation, it is proved that the essence of the weakening buffer operator (abbreviated as WBO) can weaken the disturbance of one variable. According to this, the multi-variable weakening buffer operator is put forward. The multi-variable weakening buffer operator can satisfy the desire to use the freshest data and its buffer effect is obvious when the sample size is small. Four real cases show that the proposed multi-variable weakening buffer operator has higher forecasting performances.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectForecastingen
dc.subjectGray system theoryen
dc.subjectWeakening buffer operatoren
dc.subjectMultiple linear regressionen
dc.subjectEnergy demand forecastingen
dc.titleMulti-variable weakening buffer operator and its applicationen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1016/j.ins.2016.01.002
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderLeverhulme Trust/FP7en
dc.projectidIN-2014-020en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2016-01-02en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record