원문정보
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
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