Proceedings of the 2012 National Conference on Information Technology and Computer Science

Modeling for Nonlinear Series Prediction based on the Support Vector Machine Theory

Authors
Dao-wen Liu
Corresponding Author
Dao-wen Liu
Available Online November 2012.
DOI
https://doi.org/10.2991/citcs.2012.88How to use a DOI?
Keywords
Nonlinear time series; Support Vector Machine; best parameters; prediction model
Abstract
In order to improve the prediction accuracy, applies the Support Vector Machine (SVM) theory to the prediction of the Nonlinear Series. Based on the analysis of the basic theory for the prediction, adopts the Cross Validation method to choose the best parameters and then establishes the prediction model. For the stock index of Shanghai Stock Exchange, carries out the prediction to verify the effect of the model. Proved by the research, the method based on the Support Vector Machine theory is able to reflect the changing tendencies, and has the better prediction accuracy, at the same time the feasibility is verified by the method.
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Proceedings
2012 National Conference on Information Technology and Computer Science
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2012
ISBN
978-94-91216-39-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/citcs.2012.88How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Dao-wen Liu
PY  - 2012/11
DA  - 2012/11
TI  - Modeling for Nonlinear Series Prediction based on the Support Vector Machine Theory
BT  - 2012 National Conference on Information Technology and Computer Science
PB  - Atlantis Press
SP  - 336
EP  - 339
SN  - 1951-6851
UR  - https://doi.org/10.2991/citcs.2012.88
DO  - https://doi.org/10.2991/citcs.2012.88
ID  - Liu2012/11
ER  -