USING THE SVM LEARNING METHODOLOGY, BOX-JENKINS, CAUSAL MODELS AND EXPERIMENTING WITH NON-LINEAR SV REGRESSION TO FORECASTING WAGES
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63468352%3A_____%2F12%3A%230000156" target="_blank" >RIV/63468352:_____/12:#0000156 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
USING THE SVM LEARNING METHODOLOGY, BOX-JENKINS, CAUSAL MODELS AND EXPERIMENTING WITH NON-LINEAR SV REGRESSION TO FORECASTING WAGES
Original language description
The article presents the modeling development of statistical and econometric structural parameters of average nominal wages in Slovak economy. SVMs are a set of related supervised learning methods that analyze data and recognize patterns. It is used forclassification and regression analysis. These approaches model we used for automated specification of a functional form of the model. We provide the fit of average of nominal wage models based on Box-Jenkins methodology of time series analysis and a causal model to the data. Values are loaded from quarterly's data over the period 1991-2006. This model was used as a tool to compare its approximation and to forecast abilities with those obtained using SV regression method.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
AM - Pedagogy and education
OECD FORD branch
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Result continuities
Project
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Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Acta Moraviae Scientific journal for economics, management and informatics of European polytechnic institute, Ltd. in Kunovice
ISSN
1803-7607
e-ISSN
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Volume of the periodical
5
Issue of the periodical within the volume
3
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
7
Pages from-to
101-107
UT code for WoS article
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EID of the result in the Scopus database
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