The analysis of a time series is a problem well known to statisticians. Neural networks form the basis of an entirely non-linear approach to the analysis of time series. It has been widely used in pattern recognition, classification and prediction. Recently, reviews from a statistical perspective were done by Cheng and Titterington (1994) and Ripley (1993). One of the most important properties of a neural network is its ability to learn. In neural network methodology, the data set is divided in three different sets, namely a training set, a cross-validation set, and a test set. The training set is used for training the network with the various available learning (optimisation) algorithms. Different algorithms will perform best on different problems. The advantages and limitations of different algorithms in respect of all training problems are discussed. In this dissertation the method of neural networks and that of ARlMA. models are discussed. The procedures of identification, estimation and evaluation of both models are investigated. Many of the standard techniques in statistics can be compared with neural network methodology, especially in applications with large data sets. Additional information available on two discs stored at the Africana section, Merensky Library. / Dissertation (MSc (Mathematical Statistics))--University of Pretoria, 2007. / Statistics / unrestricted
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:up/oai:repository.up.ac.za:2263/30586 |
| Date | 23 February 2007 |
| Creators | Du Plessis, K |
| Contributors | Dr H Boraine, upetd@up.ac.za, Dr J E W Holm |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Dissertation |
| Rights | © 2000, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
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