• SVM Application In Stock Trendency Forecasting Based On PSO Parameter Optimization   [MASS 2012]
  • Author(s)
  • Yun LIN, Yunsheng ZHOU
  • In recent years, the use of support vector machine classification on the stock market forecasts is of great popular. However, forecasts using SVM to do classification needs to adjust relevant parameters (mainly the penalty parameter c and the kernel function parameter g) to achieve the ideal prediction accuracy of classification. Many researchers have tried to use cross-validation (CV, Cross Validation) ideas, to find the penalty parameter c and the kernel function parameter g. Using cross- validation proved although the idea can be obtained in some sense optimal parameters, you can avoid the occurrence of the state of over-fitting and lack-of-fitting, but the use of grid search of this non-heuristic optimization, and sometimes if you want to in a larger scale search for the best c and g within the parameters will be very time-consuming. This article addresses the classification only using the training set to find the best parameters, not only to avoid traversing all the parameters within the grid points, but also efficient and high accuracy to predict the training set and test set can be reasonably predict, making the test set Classification accuracy rate is maintained at a high level, to avoid the occurrence of the state of over-fitting and lack-of-fitting. This article applies SVM parameter optimization to predicting the Shanghai Composite Index. The experimental results show that SVM method through PSO parameter optimization in financial time series has good modeling capabilities, to achieve a better prediction.
  • Financial time series; SVM; Cross-Validation; Heuristic algorithm; PSO
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