Modeling for Nonlinear Series Prediction based on the Support Vector Machine Theory
Available Online November 2012.
- https://doi.org/10.2991/citcs.2012.88How to use a DOI?
- Nonlinear time series; Support Vector Machine; best parameters; prediction model
- 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.
- 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 -