원문정보
보안공학연구지원센터(IJSIA)
International Journal of Security and Its Applications
Vol.10 No.7
2016.07
pp.269-280
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
This paper construct the predicted model based on support vector machine (SVM) for the Shanghai Composite Index, acquired the model parameters using genetic algorithm optimization was carried out, combined with k-fold cross method. Experiments based on the start date to February 2011 total 4948 trading day data, 10 fold cross circulation experiments of GA optimization; get the most accurate model parameter of SVM. At last, the regression model is used to predict, and the relative error of regression prediction is 0.11, and the accuracy of regression prediction is higher. In conclusion, this model can be used to predict the Shanghai Composite Index.
목차
Abstract
1. Introduction
2. Algorithm Analysis
2.1. The GA Algorithm Principle
2.2. SVM Regression Prediction Algorithm
3. GA-SVM Optimization of the Stock Market Forecast Model
4. Empirical Analysis
4.1. Index Selection
4.2. Experiment Design
4.3. Results Analysis
5. Conclusions
Acknowledgments
References
1. Introduction
2. Algorithm Analysis
2.1. The GA Algorithm Principle
2.2. SVM Regression Prediction Algorithm
3. GA-SVM Optimization of the Stock Market Forecast Model
4. Empirical Analysis
4.1. Index Selection
4.2. Experiment Design
4.3. Results Analysis
5. Conclusions
Acknowledgments
References
저자정보
참고문헌
자료제공 : 네이버학술정보