Modelling and forecasting of WIG20 stock index

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F14%3A86091222" target="_blank" >RIV/61989100:27510/14:86091222 - 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
Modelling and forecasting of WIG20 stock index
Original language description
We examine the ARIMA-ARCH type models for the volatility and forecasting models of Polish WIG20 stock indexes based on statistical (stochastic), machine learning methods and an intelligent methodology based on soft or granular computing and make comparisons with the class of RBF neural network and SVR 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 thanstatistical methods.
Czech name
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Czech description
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Project
<a href="/en/project/7AMB14PL029" target="_blank" >7AMB14PL029: Multiagent approach in designing enterprise systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Informační technologie pro praxi 2014 : VŠB-TUO, Faculty of Economics, 9th-10th October 2014
ISBN
978-80-248-3555-6
ISSN
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e-ISSN
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Number of pages
11
Pages from-to
89-99
Publisher name
VŠB-Technical University of Ostrava
Place of publication
Ostrava
Event location
Ostrava
Event date
Oct 9, 2014
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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