Statistical Models and Granular Soft RBF Neural Network for Malaysia KLCI Price Index Prediction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19240%2F17%3AA0000125" target="_blank" >RIV/47813059:19240/17:A0000125 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-55789-2_28" target="_blank" >http://dx.doi.org/10.1007/978-3-319-55789-2_28</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-55789-2_28" target="_blank" >10.1007/978-3-319-55789-2_28</a>
Alternative languages
Result language
angličtina
Original language name
Statistical Models and Granular Soft RBF Neural Network for Malaysia KLCI Price Index Prediction
Original language description
Two novel forecasting models are introduced to predict the data of Malaysia KLCI price index. One of them is based on Box-Jenkins methodology where the asymmetric models, i.e. EGARCH and PGARCH models were used to form the random component for ARIMA model. The other forecasting model is a soft RBF neural network with cloud Gaussian activation function in hidden layer neurons. The forecast accuracy of both models is compared by using statistical summary measures of model’s accuracy. The accuracy levels of the proposed soft neural network are better than the ARIMA/PGARCH model developed by most available statistical techniques. We found that asymmetric model with GED errors provide better predictions than with Student’s t or normal errors one. We also discuss a certain management aspect of proposed forecasting models by their use in management information systems.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Time Series Analysis and Forecasting: Selected Contributions from ITISE 2016
ISBN
9783319557892
ISSN
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e-ISSN
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Number of pages
12
Pages from-to
401-412
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
Granada; Spain
Event date
Jan 1, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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