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dc.contributor.advisorGhaziasgar, Mehrdad
dc.contributor.authorDandajena, Kudakwashe
dc.date.accessioned2023-11-29T10:07:02Z
dc.date.available2023-11-29T10:07:02Z
dc.date.issued2023
dc.identifier.urihttp://hdl.handle.net/11394/10589
dc.descriptionPhilosophiae Doctor - PhDen_US
dc.description.abstractTrial-and-error is often used for developing new deep learning models. There is no systematic procedure for improving existing models for predicting irregular sequential time-series. This research developed a systematic framework to improve state-of-the-art deep learning models for financial time-series prediction. The framework was used to create an enhanced model. A design science research methodology was used to design this framework with customised multidimensional evaluation criteria and metrics. The model was applied to predict currency exchange rates more accurately than existing state-of-the-art models found in the literature. The main contribution of this work is a framework that provides a procedure for improving deep learning models. As a proof of its usefulness, the framework was applied to develop an improved model.en_US
dc.language.isoenen_US
dc.publisherUniversity of the Western Capeen_US
dc.subjectDiscreteen_US
dc.subjectSequentialen_US
dc.subjectTime-seriesen_US
dc.subjectMachineen_US
dc.subjectModel and predictionen_US
dc.titleA framework for the analysis of discrete irregular patterned sequential datasetsen_US
dc.typeThesisen_US
dc.rights.holderUniversity of the Western Capeen_US


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