Application of Kohonen SOM Learning in Crisis Prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F17%3A00316545" target="_blank" >RIV/68407700:21340/17:00316545 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Application of Kohonen SOM Learning in Crisis Prediction

Popis výsledku v původním jazyce

The Self-Organized Mapping (SOM) is a traditional tool for multidimensional data analysis which overperforms analytical power of cluster analysis. But there are possible difficulties when the SOM is applied to data patterns of large size. Our approach macro-economical data analysis is based on logarithmic differences, pattern dimensionality reduction and finalization of data analysis using Kohonen SOM learning. This general methodology was applied to the statistic data describing the economic situation of thirty five countries during more than twenty years. The regularly published data come from statistics of European Commission. The aim is to identify similar groups of countries and characterized the similarity. The role of SOMtopology, learning strategy and reduced pattern size can be also used to crisis prediction based on similarities with countries already suffering with crisis.

Název v anglickém jazyce

Application of Kohonen SOM Learning in Crisis Prediction

Popis výsledku anglicky

The Self-Organized Mapping (SOM) is a traditional tool for multidimensional data analysis which overperforms analytical power of cluster analysis. But there are possible difficulties when the SOM is applied to data patterns of large size. Our approach macro-economical data analysis is based on logarithmic differences, pattern dimensionality reduction and finalization of data analysis using Kohonen SOM learning. This general methodology was applied to the statistic data describing the economic situation of thirty five countries during more than twenty years. The regularly published data come from statistics of European Commission. The aim is to identify similar groups of countries and characterized the similarity. The role of SOMtopology, learning strategy and reduced pattern size can be also used to crisis prediction based on similarities with countries already suffering with crisis.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Mathematical Methods in Economics MME 2017

ISBN

978-80-7435-678-0

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

254-258

Název nakladatele

Univerzita Hradec Králové

Místo vydání

Hradec Králové

Místo konání akce

Hradec Králové

Datum konání akce

13. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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