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Title: | Time series prediction using artificial neural networks: Single and multi-dimensional data | ||||||||||
Author: | Sámek, David; Vařacha, Pavel | ||||||||||
Document type: | Peer-reviewed article (English) | ||||||||||
Source document: | International Journal of Mathematical Models and Methods in Applied Sciences. 2013, vol. 7, issue 1, p. 38-46 | ||||||||||
ISSN: | 1998-0140 (Sherpa/RoMEO, JCR) | ||||||||||
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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. | ||||||||||
Full text: | http://www.naun.org/multimedia/NAUN/ijmmas/16-561.pdf | ||||||||||
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