Multi-factors time series prediction based on PCV-SVM

@article{Xiaoyun2009MultifactorsTS,
  title={Multi-factors time series prediction based on PCV-SVM},
  author={Chen Xiaoyun and Yue Min and Mu Jinchao and He Yanshan and Chen Yi},
  journal={2009 1st IEEE Symposium on Web Society},
  year={2009},
  pages={148-152}
}
Generalization performance of Support Vector Machines (SVM) is affected by parameter selection. How to select optimal parameters to achieve the best training model has been a hot research spot. In order to improve generalization performance of SVM, K-fold cross validation is used to select parameters for training. However, K-fold cross validation is time… CONTINUE READING