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dc.contributor.authorWang, Xinen_NZ
dc.date.available2011-04-07T03:16:59Z
dc.date.copyright2005-11-15en_NZ
dc.identifierhttp://adt.otago.ac.nz/public/adt-NZDU20070312.144924
dc.identifier.citationWang, X. (2005, November 15). Research of mixture of experts model for time series prediction (Thesis, Doctor of Philosophy). University of Otago. Retrieved from http://hdl.handle.net/10523/1487en
dc.identifier.urihttp://hdl.handle.net/10523/1487
dc.descriptionxxiv, 237 leaves :ill. ; 30 cm. Includes bibliographical references. University of Otago department: Information Science. "15 November 2005".
dc.description.abstractFor the prediction of chaotic time series, a dichotomy has arisen between local approaches and global approaches. Local approaches hold the reputation of simplicity and feasibility, but they generally do not produce a compact description of the underlying system and are computationally intensive. Global approaches have the advantage of requiring less computation and are able to yield a global representation of the studied time series. However, due to the complexity of the time series process, it is often not easy to construct a global model to perform the prediction precisely. In addition to these approaches, a combination of the global and local techniques, called mixture of experts (ME), is also possible, where a smaller number of models work cooperatively to implement the prediction. This thesis reports on research about ME models for chaotic time series prediction. Based on a review of the techniques in time series prediction, a HMM-based ME model called "Timeline" Hidden Markov Experts (THME) is developed, where the trajectory of the time series is divided into some regimes in the state space and regression models called local experts are applied to learn the mapping on the regimes separately. The dynamics for the expert combination is a HMM, however, the transition probabilities are designed to be time-varying and conditional on the "real time" information of the time series. For the learning of the "time-line" HMM, a modified Baum—Welch algorithm is developed and the convergence of the algorithm is proved. Different versions of the model, based on MLP, RBF and SVM experts, are constructed and applied to a number of chaotic time series on both one-step-ahead and multi-step-ahead predictions. Experiments show that in general THME achieves better generalization performance than the corresponding single models in one-step-ahead prediction and comparable to some published benchmarks in multi-step-ahead prediction. Various properties of THME, such as the feature selection for trajectory dividing, the clustering techniques for regime extraction, the "time-line" HMM for expert combination and the performance of the model when it has different number of experts, are investigated. A number of interesting future directions for this work are suggested, which include the feature selection for regime extraction, the model selection for transition probability modelling, the extension to distribution prediction and the application on other time series.en_NZ
dc.languageen
dc.publisherUniversity of Otago
dc.subjectchaotic time seriesen_NZ
dc.subject“Timeline" Hidden Markov Expertsen_NZ
dc.subjectmulti-step-ahead predictionen_NZ
dc.subjecttime-lineen_NZ
dc.subjectmodelen_NZ
dc.subjectdistribution predictionen_NZ
dc.subjecttime seriesen_NZ
dc.subjectMixture of Expertsen_NZ
dc.subjectMarkov processes
dc.subject.lcshT Technology (General)en_NZ
dc.subject.lcshQ Science (General)en_NZ
dc.subject.lcshHG Financeen_NZ
dc.subject.lcshHF5601 Accountingen_NZ
dc.titleResearch of mixture of experts model for time series predictionen_NZ
dc.typeThesisen_NZ
dc.description.versionUnpublisheden_NZ
otago.date.accession2006-09-04en_NZ
otago.schoolInformation Scienceen_NZ
thesis.degree.disciplineInformation Scienceen_NZ
thesis.degree.nameDoctor of Philosophy
thesis.degree.grantorUniversity of Otagoen_NZ
thesis.degree.levelDoctoral Thesesen_NZ
otago.interloanyesen_NZ
otago.openaccessAbstract Only
dc.identifier.eprints389en_NZ
otago.school.eprintsInformation Scienceen_NZ
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