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Neural networks for time series analysis
Dissertation (MSc (Mathematical Statistics))--University of Pretoria, 2007.
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| Format: | Thesis |
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University of Pretoria
2013
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| _version_ | 1867613665494040576 |
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| access_status_str | Open Access |
| author2 | Boraine, H. |
| author_browse | Boraine, H. |
| author_facet | Boraine, H. |
| collection | Thesis |
| dc_rights_str_mv | © 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. |
| description | Dissertation (MSc (Mathematical Statistics))--University of Pretoria, 2007. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/30586 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:39:45.647Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/30586 Neural networks for time series analysis Boraine, H. upetd@up.ac.za Holm, J.E.W. Du Plessis, K Neural networks Time-series analysis Computer science UCTD Dissertation (MSc (Mathematical Statistics))--University of Pretoria, 2007. 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. Statistics unrestricted 2013-09-07T19:22:27Z 2007-02-23 2013-09-07T19:22:27Z 2000-04-20 2007-02-23 2007-02-23 Dissertation Du Plessis, K 2000 Neural networks for time series analysis, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/30586 > H230/ag http://hdl.handle.net/2263/30586 http://upetd.up.ac.za/thesis/available/etd-02232007-095334/ © 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. application/pdf University of Pretoria |
| spellingShingle | Neural networks Time-series analysis Computer science UCTD Neural networks for time series analysis |
| title | Neural networks for time series analysis |
| title_full | Neural networks for time series analysis |
| title_fullStr | Neural networks for time series analysis |
| title_full_unstemmed | Neural networks for time series analysis |
| title_short | Neural networks for time series analysis |
| title_sort | neural networks for time series analysis |
| topic | Neural networks Time-series analysis Computer science UCTD |
| url | http://hdl.handle.net/2263/30586 http://upetd.up.ac.za/thesis/available/etd-02232007-095334/ |