Time series prediction using supervised learning and tools from chaos theory

5.00
Hdl Handle:
http://hdl.handle.net/10547/582141
Title:
Time series prediction using supervised learning and tools from chaos theory
Authors:
Edmonds, Andrew Nicola
Abstract:
In this work methods for performing time series prediction on complex real world time series are examined. In particular series exhibiting non-linear or chaotic behaviour are selected for analysis. A range of methodologies based on Takens' embedding theorem are considered and compared with more conventional methods. A novel combination of methods for determining the optimal embedding parameters are employed and tried out with multivariate financial time series data and with a complex series derived from an experiment in biotechnology. The results show that this combination of techniques provide accurate results while improving dramatically the time required to produce predictions and analyses, and eliminating a range of parameters that had hitherto been fixed empirically. The architecture and methodology of the prediction software developed is described along with design decisions and their justification. Sensitivity analyses are employed to justify the use of this combination of methods, and comparisons are made with more conventional predictive techniques and trivial predictors showing the superiority of the results generated by the work detailed in this thesis.
Citation:
Edmonds, A.N. (1996) 'Time series prediction using supervised learning and tools from chaos theory'. PhD thesis. University of Luton.
Publisher:
University of Bedfordshire
Issue Date:
Dec-1996
URI:
http://hdl.handle.net/10547/582141
Type:
Thesis or dissertation
Language:
en
Description:
A thesis submitted to the Faculty of Science and Computing, University of Luton, in partial fulfilment of the requirements for the degree of Doctor of Philosophy
Appears in Collections:
PhD e-theses

Full metadata record

DC FieldValue Language
dc.contributor.authorEdmonds, Andrew Nicolaen
dc.date.accessioned2015-11-13T10:41:13Zen
dc.date.available2015-11-13T10:41:13Zen
dc.date.issued1996-12en
dc.identifier.citationEdmonds, A.N. (1996) 'Time series prediction using supervised learning and tools from chaos theory'. PhD thesis. University of Luton.en
dc.identifier.urihttp://hdl.handle.net/10547/582141en
dc.descriptionA thesis submitted to the Faculty of Science and Computing, University of Luton, in partial fulfilment of the requirements for the degree of Doctor of Philosophyen
dc.description.abstractIn this work methods for performing time series prediction on complex real world time series are examined. In particular series exhibiting non-linear or chaotic behaviour are selected for analysis. A range of methodologies based on Takens' embedding theorem are considered and compared with more conventional methods. A novel combination of methods for determining the optimal embedding parameters are employed and tried out with multivariate financial time series data and with a complex series derived from an experiment in biotechnology. The results show that this combination of techniques provide accurate results while improving dramatically the time required to produce predictions and analyses, and eliminating a range of parameters that had hitherto been fixed empirically. The architecture and methodology of the prediction software developed is described along with design decisions and their justification. Sensitivity analyses are employed to justify the use of this combination of methods, and comparisons are made with more conventional predictive techniques and trivial predictors showing the superiority of the results generated by the work detailed in this thesis.en
dc.language.isoenen
dc.publisherUniversity of Bedfordshireen
dc.subjectG990 Mathematical and Computing Sciences not elsewhere classifieden
dc.subjectchaos theoryen
dc.subjecttime series predictionen
dc.titleTime series prediction using supervised learning and tools from chaos theoryen
dc.typeThesis or dissertationen
dc.type.qualificationnamePhDen_GB
dc.type.qualificationlevelPhDen
dc.publisher.institutionUniversity of Bedfordshireen
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