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Recurrent neural networks for time-series prediction.
University of Skövde, Department of Computer Science.
2000 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesis
Abstract [en]

Recurrent neural networks have been used for time-series prediction with good results. In this dissertation recurrent neural networks are compared with time-delayed feed forward networks, feed forward networks and linear regression models on a prediction task. The data used in all experiments is real-world sales data containing two kinds of segments: campaign segments and non-campaign segments. The task is to make predictions of sales under campaigns. It is evaluated if more accurate predictions can be made when only using the campaign segments of the data.

Throughout the entire project a knowledge discovery process, identified in the literature has been used to give a structured work-process. The results show that the recurrent network is not better than the other evaluated algorithms, in fact, the time-delayed feed forward neural network showed to give the best predictions. The results also show that more accurate predictions could be made when only using information from campaign segments.

Place, publisher, year, edition, pages
Skövde: Institutionen för datavetenskap , 2000. , 79 p.
Keyword [en]
KDD, Data mining, Recurrent neural networks, Time-series prediction
National Category
Information Science
Identifiers
URN: urn:nbn:se:his:diva-480OAI: oai:DiVA.org:his-480DiVA: diva2:2859
Presentation
(English)
Uppsok
samhälle/juridik
Supervisors
Available from: 2008-01-11 Created: 2008-01-11 Last updated: 2009-05-15

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fulltext(376 kB)738 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf