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

Result code in 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>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

AH - Economics

OECD FORD branch

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Project

<a href="/en/project/GA402%2F08%2F0022" target="_blank" >GA402/08/0022: The Latest Intelligent Methodologies for Economic Time Series Modelling and Forecasting</a><br>

Continuities

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

Publication year

2009

Confidentiality

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

Name of the periodical

International Journal of Computational Intelligence Systems (IJCIS)

ISSN

1875-6883

e-ISSN

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Volume of the periodical

Vol. 2-4

Issue of the periodical within the volume

12/2008

Country of publishing house

BE - BELGIUM

Number of pages

12

Pages from-to

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UT code for WoS article

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EID of the result in the Scopus database

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