HYBRID SOM-SVM ALGORITHM FOR REAL TIME SERIES FORECASTING

J. M. Górriz, C. G. Puntonet, E. W. Lang

Abstract

In this paper we show a new on-line parametric model for time series forecasting based on Vapnik- Chervonenkis (VC) theory. Using the strong connection between support vector machines (SVM) and Regularization theory (RT), we propose a regularization operator in order to obtain a suitable expansion of radial basis functions (RBFs) with the corresponding expressions for updating neural parameters. This operator seeks for the “attest” function in a feature space, minimizing the risk functional. Finally we mention some modifications and extensions that can be applied to control neural resources and select relevant input space.

References

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Paper Citation


in Harvard Style

Górriz J., Puntonet C. and Lang E. (2004). HYBRID SOM-SVM ALGORITHM FOR REAL TIME SERIES FORECASTING . In Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 972-8865-12-0, pages 103-107. DOI: 10.5220/0001124301030107


in Bibtex Style

@conference{icinco04,
author={J. M. Górriz and C. G. Puntonet and E. W. Lang},
title={HYBRID SOM-SVM ALGORITHM FOR REAL TIME SERIES FORECASTING},
booktitle={Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2004},
pages={103-107},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001124301030107},
isbn={972-8865-12-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - HYBRID SOM-SVM ALGORITHM FOR REAL TIME SERIES FORECASTING
SN - 972-8865-12-0
AU - Górriz J.
AU - Puntonet C.
AU - Lang E.
PY - 2004
SP - 103
EP - 107
DO - 10.5220/0001124301030107