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Conference Paper: Application of artificial neural networks in sales forecasting

TitleApplication of artificial neural networks in sales forecasting
Authors
KeywordsSales Forecasting
Artificial Neural Networks
Back-propagation
Genetic Algorithms
Issue Date1997
PublisherIEEE.
Citation
International Conference on Neural Networks Proceedings, Houston, USA, 9-12 June 1997, v. 4, p. 2121-2124 How to Cite?
AbstractThe aim of the work presented in this paper is to forecast sales volumes as accurately as possible and as far into the future as possible. The choice of network topology was Silva's adaptive backpropagation algorithm and the network architectures were selected by genetic algorithms (GAs). The networks were trained to forecast from 1 month to 6 months in advance and the performance of the network was tested after training. The test results of artificial neural networks (ANNs) are compared with the time series smoothing methods of forecasting using several measures of accuracy. The outcome of the comparison proved that the ANNs generally perform better than the time series smoothing methods of forecasting. Further recommendations resulting from this paper are presented
Persistent Identifierhttp://hdl.handle.net/10722/46579
ISSN

 

DC FieldValueLanguage
dc.contributor.authorYip, DHFen_HK
dc.contributor.authorHines, ELen_HK
dc.contributor.authorYu, WWHen_HK
dc.date.accessioned2007-10-30T06:53:20Z-
dc.date.available2007-10-30T06:53:20Z-
dc.date.issued1997en_HK
dc.identifier.citationInternational Conference on Neural Networks Proceedings, Houston, USA, 9-12 June 1997, v. 4, p. 2121-2124en_HK
dc.identifier.issn1098-7576en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46579-
dc.description.abstractThe aim of the work presented in this paper is to forecast sales volumes as accurately as possible and as far into the future as possible. The choice of network topology was Silva's adaptive backpropagation algorithm and the network architectures were selected by genetic algorithms (GAs). The networks were trained to forecast from 1 month to 6 months in advance and the performance of the network was tested after training. The test results of artificial neural networks (ANNs) are compared with the time series smoothing methods of forecasting using several measures of accuracy. The outcome of the comparison proved that the ANNs generally perform better than the time series smoothing methods of forecasting. Further recommendations resulting from this paper are presenteden_HK
dc.format.extent403953 bytes-
dc.format.extent1774 bytes-
dc.format.extent3380 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectSales Forecastingen_HK
dc.subjectArtificial Neural Networksen_HK
dc.subjectBack-propagationen_HK
dc.subjectGenetic Algorithmsen_HK
dc.titleApplication of artificial neural networks in sales forecastingen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1098-7576&volume=4&spage=2121&epage=2124&date=1997&atitle=Application+of+artificial+neural+networks+in+sales+forecastingen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICNN.1997.614233en_HK
dc.identifier.hkuros29101-

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