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

Kód výsledku v 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>

Výsledek na webu

<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

—

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings ITISE 2016

ISBN

978-84-16478-93-4

ISSN

—

e-ISSN

—

Počet stran výsledku

11

Strana od-do

530-540

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Granada

Datum konání akce

27. 6. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—