期刊文献+

金融时间序列预测模型——基于离散小波分解与支持向量回归的研究 被引量:8

下载PDF
导出
摘要 建本研究结合小波转换与支持向量回归,提出一个二阶段时间序列预测模型。先以离散小波分解与重组对金融时间序列数据进行预处理,再以SVR建立预测模型。
出处 《统计与决策》 CSSCI 北大核心 2007年第14期4-7,共4页 Statistics & Decision
  • 相关文献

参考文献11

  • 1Cherkassky, V. and Y. Ma. Practical selection of SVM Parameters and Noise Estimation for SVM Regression [J]. Neural Networks, 2004,17 : 113-126.
  • 2Cherkassky, V. and F. Mulier. Vapnik-Chervonenkis (VC) Learning Theory and Its Applications[J]. IEEE Transactions on Neural Networks,1999,10:985-987.
  • 3Drucker, H., C. J. C. Burges, L. Kaufman, A. Smola, and V. N. Vapnik. Support Vector Regression Machines [J]. Advances in Neural Information Processing Systems,1997,9:155-161.
  • 4Hsu, C. W., C. C. Lin, and C. J. Lin.A Practical Guide to Support Classification[DB/OL] Available from: http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf,2003.
  • 5Lee, T. S. and N. J. Chen. Investigating the Information Content of Non-cash-trading Index Futures Using Neural Networks [J]. Expert Systems with Applications, 2002,22:225-234.
  • 6Lee, T. S., N. J. Chen, and C. C. Chiu. Forecasting the Opening Cash Price Index Using Grey Forecasting and Neural Networks: Evidence from the SGX-DT MSCI Taiwan Index Futures Contracts[M]. Wang, P and Chen, S. S. (Eds), Computational Intelligence in Economics and Finance, Springer, 2003:151-170.
  • 7Vapnik, V.. The Nature of Statistical Learning Theory (2nded.) [M]. Berlin:Springer,1999.
  • 8Vapnik, V. N., S. Golowich, and A. J. Smola. Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing [J]. Advances in Neural Information Processing Systems,1997,9:281-287.
  • 9Wang, C. Z. and S. X. Wang. Supporting Content-based Searches on Time Series Via Approximation [C]. Proceedings of 12th International Conference on Scientific and Statistical Database Management, 2000:69-81.
  • 10Yang, Y. and X. Liu. A Re-examination of Text Categorization Methods[C]. ACM International Conference on Research and Development in Information Retrieval,1999,22: 42-49.

二级参考文献6

共引文献13

同被引文献89

引证文献8

二级引证文献17

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部