Wavelet-based relevance vector machines for stock index forecasting

研究成果: Conference contribution

3 引文 斯高帕斯(Scopus)

摘要

Relevance vector machine (RVM) is a Beyesian version of the support vector machine, which with a sparse model representation, has appeared as a powerful tool for time series forecasting. RVM has demonstrated better performance over other methods such as neural networks or ARIMA-based models. This paper proposes a wavelet-based RVM model to forecast stock indices. The time series of explanatory variables are decomposed by the wavelet basis, and the extracted time scale features served as inputs of a RVM to perform the nonparametric regression and forecasting. Compared with the traditional GARCH model forecasts, the new method shows superior performance, and reduces the root-mean-squared forecasting errors by nearly one order.

原文English
主出版物標題International Joint Conference on Neural Networks 2006, IJCNN '06
頁面603-609
頁數7
出版狀態Published - 2006 十二月 1
事件International Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
持續時間: 2006 七月 162006 七月 21

Other

OtherInternational Joint Conference on Neural Networks 2006, IJCNN '06
國家Canada
城市Vancouver, BC
期間06-07-1606-07-21

    指紋

All Science Journal Classification (ASJC) codes

  • Software

引用此

Huang, S-C., & Wu, T-K. (2006). Wavelet-based relevance vector machines for stock index forecasting. 於 International Joint Conference on Neural Networks 2006, IJCNN '06 (頁 603-609). [1716149]