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Statements

Subject Item
n2:RIV%2F47813059%3A19240%2F06%3A%230000170%21RIV07-GA0-19240___
rdf:type
n15:Vysledek skos:Concept
dcterms:description
The general methodological framework of classical econometric approach and soft computing approach are considered to forecast economic time series. Some theoretical concepts are discussed. The article introduces the concept of econometric and time series modelling, demonstrates some of the classical and later econometric models, discusses the use of decomposition techniques to model and forecast time series described by trend and seasonal components. The use of co-integration concept is discussed as well. The autoregressive and direct smoothing procedures and transfer function models and several applications illustrating these approaches in practical situations are introduced and presented. Finally, we introduce and examine the use of novel-modelling techniques such as learning from experimental data (statistical learning) neural networks and fuzzy logic methods. Článek celkově pojednává o klasických ekonometrických metodech a o soft computingových technikách s využitím na predikci ekonomických časových řad. Popisuje se koncept ekonometrického a statistického modelování, prezentují se klasické a novější modely, diskutuje se použití dekompozičních metod založených na trendovém a sezónním komponentu a koncept kointegrace pro modelování a predikci časových řad. Dále se uvádějí autoregresívní a vyhlazovací procedury, modely přenosových funkcí s odkazy na několik aplikací, které ilustrují využití těchto technik. Nakonec se pojednává o použití nejnovějších modelovacích přístupech jako jsou učení z experimentálních dat (statistical learning), neuronové sítě a metody založené na fuzzy logice (fuzzy množinách). The general methodological framework of classical econometric approach and soft computing approach are considered to forecast economic time series. Some theoretical concepts are discussed. The article introduces the concept of econometric and time series modelling, demonstrates some of the classical and later econometric models, discusses the use of decomposition techniques to model and forecast time series described by trend and seasonal components. The use of co-integration concept is discussed as well. The autoregressive and direct smoothing procedures and transfer function models and several applications illustrating these approaches in practical situations are introduced and presented. Finally, we introduce and examine the use of novel-modelling techniques such as learning from experimental data (statistical learning) neural networks and fuzzy logic methods.
dcterms:title
Kvantitatívní nástroje a soft computing v manažerských informačních systémech Quantitative tools and soft computing in management information systems Quantitative tools and soft computing in management information systems
skos:prefLabel
Quantitative tools and soft computing in management information systems Kvantitatívní nástroje a soft computing v manažerských informačních systémech Quantitative tools and soft computing in management information systems
skos:notation
RIV/47813059:19240/06:#0000170!RIV07-GA0-19240___
n5:strany
23-34
n5:aktivita
n8:P
n5:aktivity
P(GA402/05/2768)
n5:cisloPeriodika
1
n5:dodaniDat
n14:2007
n5:domaciTvurceVysledku
n18:3097331
n5:druhVysledku
n6:J
n5:duvernostUdaju
n16:S
n5:entitaPredkladatele
n7:predkladatel
n5:idSjednocenehoVysledku
496204
n5:idVysledku
RIV/47813059:19240/06:#0000170
n5:jazykVysledku
n17:eng
n5:klicovaSlova
SC techniques; econometric modelling; management information and prediction systems
n5:klicoveSlovo
n12:SC%20techniques n12:econometric%20modelling n12:management%20information%20and%20prediction%20systems
n5:kodStatuVydavatele
SK - Slovenská republika
n5:kontrolniKodProRIV
[2B7C63210712]
n5:nazevZdroje
Jounal of Information, Control and Management Sytems
n5:obor
n11:AH
n5:pocetDomacichTvurcuVysledku
1
n5:pocetTvurcuVysledku
2
n5:projekt
n10:GA402%2F05%2F2768
n5:rokUplatneniVysledku
n14:2006
n5:svazekPeriodika
4
n5:tvurceVysledku
Marček, Dušan Marček, Millan
s:issn
1336-1716
s:numberOfPages
12
n13:organizacniJednotka
19240