Granular RBF NN Approach and Statistical Methods Applied to Modelling and Forecasting High Frequency Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19240%2F09%3A%230002964" target="_blank" >RIV/47813059:19240/09:#0002964 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Granular RBF NN Approach and Statistical Methods Applied to Modelling and Forecasting High Frequency Data

Popis výsledku v původním jazyce

We examine the ARCH-GARCH models for the forecasting of the bond price time series provided by VUB bank and make comparisons the forecast accuracy with the class of RBF neural network models. A limited statistical or computer science theory exists on howto design the architecture of RBF networks for some specific nonlinear time series, which allows for exhaustive study of the underlying dynamics, and determine their parameters. To illustrate the forecasting performance of these approaches the learningaspects of RBF networks are presented and an application is included. 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 modelled by cloud concept. Ina comparative study is shown that the presented approach is able to model and predict high frequency data with reasonable accuracy and more efficient than statistical methods.

Název v anglickém jazyce

Granular RBF NN Approach and Statistical Methods Applied to Modelling and Forecasting High Frequency Data

Popis výsledku anglicky

We examine the ARCH-GARCH models for the forecasting of the bond price time series provided by VUB bank and make comparisons the forecast accuracy with the class of RBF neural network models. A limited statistical or computer science theory exists on howto design the architecture of RBF networks for some specific nonlinear time series, which allows for exhaustive study of the underlying dynamics, and determine their parameters. To illustrate the forecasting performance of these approaches the learningaspects of RBF networks are presented and an application is included. 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 modelled by cloud concept. Ina comparative study is shown that the presented approach is able to model and predict high frequency data with reasonable accuracy and more efficient than statistical methods.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

AH - Ekonomie

OECD FORD obor

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Projekt

<a href="/cs/project/GA402%2F08%2F0022" target="_blank" >GA402/08/0022: Nejnovější inteligentní metodologie pro modelování a predikci ekonomických časových řad</a><br>

Návaznosti

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

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název periodika

International Journal of Computational Intelligence Systems (IJCIS)

ISSN

1875-6883

e-ISSN

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Svazek periodika

Vol. 2-4

Číslo periodika v rámci svazku

12/2008

Stát vydavatele periodika

BE - Belgické království

Počet stran výsledku

12

Strana od-do

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Kód UT WoS článku

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EID výsledku v databázi Scopus

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