Forecast of Consumer Behaviour Based on Neural Networks Models Comparison.

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F12%3A00200599" target="_blank" >RIV/62156489:43110/12:00200599 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Forecast of Consumer Behaviour Based on Neural Networks Models Comparison.
Original language description
The aim of this article is comparison of accuracy level of forecasted values of several artificial neural network models. The comparison is performed on datasets of Czech household consumption values. Several statistical models en resolve this task withmore or fewer restrictions. In previous work where models' input conditions were not so strict and model with missing data was used (the time series didn't contain many values) we have obtained comparably good results with artificial neural networks. Twoviews - practical and theoretical, motivate the purpose of this study. Forecasting models for medium term prognosis of the main trends of Czech household consumption is part of the faculty research design grant MSM 6215648904/03/02 (Sub-task 5.3) whichdefines the practical purpose. Testing of nonlinear autoregressive artificial neural network model compared with feedforward neural network and radial basis function neural network defines the theoretical purpose. The performance metrics
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
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis
ISSN
1211-8516
e-ISSN
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Volume of the periodical
2012
Issue of the periodical within the volume
2
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
6
Pages from-to
437-442
UT code for WoS article
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EID of the result in the Scopus database
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