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dc.contributor.authorWang, Xinen_NZ
dc.contributor.authorWhigham, Peter Aen_NZ
dc.contributor.authorDeng, Daen_NZ
dc.date.available2011-04-07T03:06:31Z
dc.date.copyright2003-06en_NZ
dc.identifier.citationWang, X., Whigham, P. A., & Deng, D. (2003). Time-line hidden Markov experts and its application in time series prediction (Information Science Discussion Papers Series No. 2003/03). Information Science. Retrieved from http://hdl.handle.net/10523/1097en
dc.identifier.urihttp://hdl.handle.net/10523/1097
dc.description.abstractA modularised connectionist model, based on the Mixture of Experts (ME) algorithm for time series prediction, is introduced. A set of connectionist modules learn to be local experts over some commonly appearing states of a time series. The dynamics for mixing the experts is a Markov process, in which the states of a time series are regarded as states of a HMM. Hence, there is a Markov chain along the time series and each state associates to a local expert. The state transition on the Markov chain is the process of activating a different local expert or activating some of them simultaneously by different probabilities generated from the HMM. The state transition property in the HMM is designed to be time-variant and conditional on the first order dynamics of the time series. A modified Baum–Welch algorithm is introduced for the training of the time-variant HMM and it has been proved that by EM process the likelihood function will converge to a local minimum. Experiments, with two time series, show this approach achieves significant improvement in the generalisation performance over global models.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherInformation Scienceen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subjecttime series predictionen_NZ
dc.subjectMixture of Expertsen_NZ
dc.subjectHMMen_NZ
dc.subjectconnectionist modelen_NZ
dc.subjectexpectation and maximizationen_NZ
dc.subjectGauss probability density distributionen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleTime-line hidden Markov experts and its application in time series predictionen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages21en_NZ
otago.date.accession2006-02-22en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints264en_NZ
otago.school.eprintsInformation Scienceen_NZ
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otago.relation.number2003/03en_NZ
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