International Journal of Computational Intelligence Systems

Volume 2, Issue 4, December 2009, Pages 353 - 364

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

Authors
Dusan Marcek, Milan Marcek, Jan Babel
Corresponding Author
Dusan Marcek
Available Online 19 December 2009.
DOI
https://doi.org/10.2991/ijcis.2009.2.4.4How to use a DOI?
Keywords
Time series, classes of ARCH-GARCH models, volatility, forecasting, neural networks, cloud concept, forecast accuracy.
Abstract
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 how to design the architecture of RBF networks for some specific nonlinear time series, which allows for exhaustive study of the underlying dynamics, and determination of their parameters. To illustrate the forecasting performance of these approaches the learning aspects 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. In a 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.
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This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
2 - 4
Pages
353 - 364
Publication Date
2009/12
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.2009.2.4.4How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Dusan Marcek
AU  - Milan Marcek
AU  - Jan Babel
PY  - 2009
DA  - 2009/12
TI  - Granular RBF NN Approach and Statistical Methods Applied to Modelling and Forecasting High Frequency Data
JO  - International Journal of Computational Intelligence Systems
SP  - 353
EP  - 364
VL  - 2
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2009.2.4.4
DO  - https://doi.org/10.2991/ijcis.2009.2.4.4
ID  - Marcek2009
ER  -