A Gray Model With a Time Varying Weighted Generating Operator

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dc.contributor.author Wu, L. en
dc.contributor.author Liu, S. en
dc.contributor.author Yang, Yingjie en
dc.date.accessioned 2016-03-24T15:26:21Z
dc.date.available 2016-03-24T15:26:21Z
dc.date.issued 2016-03-06
dc.identifier.citation Wu, L., Liu, S. and Yang, Y. (2016) A Gray model with a time varying weighted generating operator. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46, pp.427 - 433 en
dc.identifier.issn 2168-2216
dc.identifier.uri http://hdl.handle.net/2086/11714
dc.description The file attached to this record is the authors final peer reviewed version. The publisher's version of Record can be found by following the DOI link. en
dc.description.abstract A grey model with time varying weighted generating operator is put forward in order to fully extract information concealed in recent data. This model increases the weight of new data and relieve the influence of the the data fluctuation. The relationship between the sample size and the error from the inverse time varying weighted generating operator is discussed. Compared with traditional grey forecasting models, the results of practical numerical examples demonstrate that this grey model performs well in forecasting problems with limited data, and can provide reliable and acceptable accuracy for future prediction. en
dc.language.iso en en
dc.publisher IEEE en
dc.subject Grey forecasting en
dc.subject grey generating operator en
dc.subject failure frequency forecasting en
dc.subject electricity consumption en
dc.subject tuberculosis incidence en
dc.title A Gray Model With a Time Varying Weighted Generating Operator en
dc.type Article en
dc.identifier.doi http://dx.doi.org/10.1109/TSMC.2015.2426133
dc.researchgroup Centre for Computational Intelligence en
dc.peerreviewed Yes en
dc.funder Leverhulme Trust en
dc.projectid IN-2014-020 en

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