Statistical Models and Granular Soft RBF Neural Network for Malaysia KLCI Price Index Prediction

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Statistical Models and Granular Soft RBF Neural Network for Malaysia KLCI Price Index Prediction

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Statistical Models and Granular Soft RBF Neural Network for Malaysia KLCI Price Index Prediction

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

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

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Time Series Analysis and Forecasting: Selected Contributions from ITISE 2016

ISBN

9783319557892

ISSN

—

e-ISSN

—

Počet stran výsledku

12

Strana od-do

401-412

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Místo konání akce

Granada; Spain

Datum konání akce

1. 1. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—