HYBRID SOM-SVM ALGORITHM FOR REAL TIME SERIES FORECASTING

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

2004

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.

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