Earticle

초록

영어

In recent years, data mining techniques such as neural networks, support vector Regression have been applied extensively to the task of predicting financial variables. As influenced by various factors, the volatility of stock shows a non-linear characteristic, which demonstrates that the forecasting is a non-linear problem. Support vector regression (SVR) is proven to be useful in dealing with non-linear forecasting problems in recent years. The key point in using SVR for forecasting is how to determine the appropriate parameters. An improved Artificial Neural Networks(ANN) algorithm is used to optimize the parameter set of (C, σ), which influences the performance of this model directly. By doing so, this model can deal with the nonlinearity and multi-factors of volatility, and ensure stability and accuracy of support vector machine based regression. Finally, we study a case with the satisfactory result by the SPA test which is showing that this model is more accurate than other models, which guarantees its application.

목차

Abstract
 1. Introduction
 2. SVR Providing the Oretical Foundation for Structure and Parameters of RBF
 3. GA Providing SVR Models Parameters
 4. SVR Providing Network Structure and Parameters for RBF
 5. The SPA Test
 6. Case Study
  6.1. Selection of Trained Sample Data
  6.2. ANNSVR−Prediction Model using trained Sample Data
  6.3. SPA Test
 7. Conclusion
 Acknowledgements
 References

키워드

저자

• Liqiang Hou [ School of Management of Hefei University of Technology ]
• Shanlin Yang [ School of Management of Hefei University of Technology ]
• Zhiqiang Chen [ School of Management of Hefei University of Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605