Stastistical Models and Granular Soft RBF Neural Network for Malaysia KLCI Prediction

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19240%2F16%3AN0000107" target="_blank" >RIV/47813059:19240/16:N0000107 - isvavai.cz</a>
Result on the web
<a href="http://itise.ugr.es/proceedings2016/Proceedings_ITISE2016.pdf" target="_blank" >http://itise.ugr.es/proceedings2016/Proceedings_ITISE2016.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Stastistical Models and Granular Soft RBF Neural Network for Malaysia KLCI Prediction
Original language description
Two novel forecasting models are introduced to predict the data of Malaysia KLCI. 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 level of the proposed soft neural network is better than the ARIMA/PGARCH model developed by most available statistical techniques. We found that asymmetric model with GED errors provide a better prediction than with Student´s t or normal errors one. We also discuss 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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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)

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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