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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F13%3A43869746" target="_blank" >RIV/70883521:28140/13:43869746 - isvavai.cz</a>
Alternative codes found
RIV/70883521:28110/13:43869746
Result on the web
<a href="http://www.naun.org/multimedia/NAUN/ijmmas/16-561.pdf" target="_blank" >http://www.naun.org/multimedia/NAUN/ijmmas/16-561.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Time series prediction using artificial neural networks: Single and multi-dimensional data
Original language description
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.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical
International Journal of Mathematical Models and Methods in Applied Science
ISSN
1998-0140
e-ISSN
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Volume of the periodical
7
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
9
Pages from-to
38-46
UT code for WoS article
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EID of the result in the Scopus database
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