Forecasting Models for WIG20 Index Based on Advenced Statistical Models vs. SC Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F14%3A86095864" target="_blank" >RIV/61989100:27510/14:86095864 - 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
Forecasting Models for WIG20 Index Based on Advenced Statistical Models vs. SC Models
Original language description
In this paper, we consider the accuracy of forecasting models based on statistical (stochastic), machine learning methods and an intelligent methodology based on soft or granular computing. In this paper, we illustrate the ARCH-GARCH methodology on the developing a forecast model for time series of polish WIG20 stock indexes and make comparisons the forecast accuracy with the class of RBF neural network models. To illustrate the forecasting performance of these approaches the learning aspects of RBF networks are presented. We show a new approach of function estimation for nonlinear time series model by means of a granular neural network based on Gaussian activation function modeled by cloud concept. In a comparative study is shown that the presented approach is able to model and predict high frequency data with reasonable accuracy and more efficient than statistical methods.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/EE2.3.20.0296" target="_blank" >EE2.3.20.0296: Research team for modelling of economic and financial processes at VSB-TU Ostrava</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Managing and Modeling of Financial Risks : 7th international scientific conference : proceedings : 8th-9th September 2014, Ostrava, Czech Republic. [Part I-III]
ISBN
978-80-248-3631-7
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
470-477
Publisher name
VŠB - Technical University of Ostrava
Place of publication
Ostrava
Event location
Ostrava
Event date
Sep 8, 2014
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
000350605800059