Volume 2, Issue 4, December 2009, Pages 353 - 364
Granular RBF NN Approach and Statistical Methods Applied to Modelling and Forecasting High Frequency Data
Dusan Marcek, Milan Marcek, Jan Babel
Received 14 January 2009, Accepted 12 May 2009, Available Online 1 December 2009.
- https://doi.org/10.2991/ijcis.2009.2.4.4How to use a DOI?
- Time series, classes of ARCH-GARCH models, volatility, forecasting, neural networks, cloud concept, forecast accuracy.
- 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.
- 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 -