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Time series prediction using artificial neural networks: Single and multi-dimensional data

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dc.title Time series prediction using artificial neural networks: Single and multi-dimensional data en
dc.contributor.author Sámek, David
dc.contributor.author Vařacha, Pavel
dc.relation.ispartof International Journal of Mathematical Models and Methods in Applied Sciences
dc.identifier.issn 1998-0140 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2013
utb.relation.volume 7
utb.relation.issue 1
dc.citation.spage 38
dc.citation.epage 46
dc.type article
dc.language.iso en
dc.publisher North Atlantic University Union (NAUN) en
dc.relation.uri http://www.naun.org/multimedia/NAUN/ijmmas/16-561.pdf
dc.subject Artificial neural network en
dc.subject Benchmark en
dc.subject Multi-dimensional data en
dc.subject Prediction en
dc.subject Time series en
dc.description.abstract The paper studies time series prediction using artificial neural networks. The special attention is paid to the influence of size of the input vector length. Furthermore, the prediction of standard single-dimensional data signal and the prediction of multi-dimensional data signal are compared. The tested artificial networks are as follows: multilayer feed-forward neural network, recurrent Elman neural network, adaptive linear network and radial basis function neural network. en
utb.faculty Faculty of Technology
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1003087
utb.identifier.obdid 43869896
utb.identifier.scopus 2-s2.0-84872129584
utb.source j-scopus
dc.date.accessioned 2013-02-02T01:12:48Z
dc.date.available 2013-02-02T01:12:48Z
utb.contributor.internalauthor Sámek, David
utb.contributor.internalauthor Vařacha, Pavel
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