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dc.contributor.authorPears, R
dc.contributor.authorWidiputra, H
dc.contributor.authorKasabov, N
dc.date.accessioned2014-03-21T00:48:54Z
dc.date.available2014-03-21T00:48:54Z
dc.date.copyright2013
dc.date.issued2014-03-21
dc.identifier.citationEvolving Systems, vol.4(2), pp.99 - 117 (19)
dc.identifier.issn1868-6478
dc.identifier.urihttp://hdl.handle.net/10292/7002
dc.description.abstractTime series prediction has been extensively researched in both the statistical and computational intelligence literature with robust methods being developed that can be applied across any given application domain. A much less researched problem is multiple time series prediction where the objective is to simultaneously forecast the values of multiple variables which interact with each other in time varying amounts continuously over time. In this paper we describe the use of a novel Integrated Multi-Model Framework (IMMF) that combined models developed at three di erent levels of data granularity, namely the Global, Local and Transductive models to perform multiple time series prediction. The IMMF is implemented by training a neural network to assign relative weights to predictions from the models at the three di erent levels of data granularity. Our experimental results indicate that IMMF signi cantly outperforms well established methods of time series prediction when applied to the multiple time series prediction problem.
dc.publisherSpringer
dc.relation.urihttp://dx.doi.org/10.1007/s12530-012-9069-y
dc.rightsAn author may self-archive an author-created version of his/her article on his/her own website and or in his/her institutional repository. He/she may also deposit this version on his/her funder’s or funder’s designated repository at the funder’s request or as a result of a legal obligation, provided it is not made publicly available until 12 months after official publication. He/ she may not use the publisher's PDF version, which is posted on www.springerlink.com, for the purpose of self-archiving or deposit. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at www.springerlink.com”. (Please also see Publisher’s Version and Citation).
dc.subjectDENFIS
dc.subjectLocal trend models
dc.subjectDynamic interaction networks
dc.subjectIntegrated multi model framework
dc.subjectTransductive modelling
dc.titleEvolving integrated multi-model framework for on line multiple time series prediction
dc.typeJournal Article
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.1007/s12530-012-9069-y
aut.relation.endpage117
aut.relation.issue2
aut.relation.pages19
aut.relation.startpage99
aut.relation.volume4
pubs.elements-id149068


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