Machine Learning for Decision-Support in Distributed Networks

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dc.contributor.author Setati, Makgopa Gareth
dc.date.accessioned 2006-11-14T08:33:57Z
dc.date.available 2006-11-14T08:33:57Z
dc.date.issued 2006-11-14T08:33:57Z
dc.identifier.uri http://hdl.handle.net/10539/1663
dc.description Student Number : 9801145J - MSc dissertation - School of Electrical and Information Engineering - Faculty of Engineering en
dc.description.abstract In this document, a paper is presented that reports on the optimisation of a system that assists in time series prediction. Daily closing prices of a stock are used as the time series under which the system is being optimised. Concepts of machine learning, Artificial Neural Networks, Genetic Algorithms, and Agent-Based Modeling are used as tools for this task. Neural networks serve as the prediction engine and genetic algorithms are used for optimisation tasks as well as the simulation of a multi-agent based trading environment. The simulated trading environment is used to ascertain and optimise the best data, in terms of quality, to use as inputs to the neural network. The results achieved were positive and a large portion of this work concentrates on the refinement of the predictive capability. From this study it is concluded that AI methods bring a sound scientific approach to time series prediction, regardless of the phenomena that is being predicted. en
dc.format.extent 598236 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.subject optimisation en
dc.subject time series prediction en
dc.subject machine learning en
dc.subject Artificial Neural Networks en
dc.subject Genetic Algorithms en
dc.subject Agent-Based Modeling en
dc.subject prediction engine en
dc.title Machine Learning for Decision-Support in Distributed Networks en
dc.type Thesis en


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