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Detail publikace
ŠTENCL, M. ŠŤASTNÝ, J.
Originální název
Advanced approach to numerical forecasting using artificial neural networks
Typ
článek v časopise - ostatní, Jost
Jazyk
angličtina
Originální abstrakt
Current global market is driven by many factors, such as the information age, the time and amount of information distributed by many data channels it is practically impossible analyze all kinds of incoming information flows and transform them to data with classical methods. New requirements could be met by using other methods. Once trained on patterns artificial neural networks can be used for forecasting and they are able to work with extremely big data sets in reasonable time. The patterns used for learning process are samples of past data. This paper uses Radial Basis Functions neural network in comparison with Multi Layer Perceptron network with Back-propagation learning algorithm on prediction task. The task works with simplified numerical time series and includes forty observations with prediction for next five observations. The main topic of the article is the identification of the main differences between used neural networks architectures together with numerical forecasting. Detected differences then verify on practical comparative example.
Klíčová slova
Artificial Neural Networks, Multi Layer Perceptron Network, Numerical Forecasting, Radial basis function
Autoři
ŠTENCL, M.; ŠŤASTNÝ, J.
Rok RIV
2009
Vydáno
21. 12. 2009
ISSN
1211-8516
Periodikum
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis
Ročník
Číslo
6
Stát
Česká republika
Strany od
297
Strany do
305
Strany počet
8
BibTex
@article{BUT48389, author="Michael {Štencl} and Jiří {Šťastný}", title="Advanced approach to numerical forecasting using artificial neural networks", journal="Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis", year="2009", volume="2009", number="6", pages="297--305", issn="1211-8516" }