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

DOI

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.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 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  - 10.2991/citcs.2012.88
ID  - Liu2012/11
ER  -