資料載入中.....
|
請使用永久網址來引用或連結此文件:
http://ir.ncue.edu.tw/ir/handle/987654321/15958
|
題名: | Wavelet-Based Relevance Vector Machines for Stock Index Forecasting |
作者: | Huang, Shian-Chang;Wu, Tung-Kuang |
貢獻者: | 資訊管理學系 |
日期: | 2006-07
|
上傳時間: | 2013-04-22T07:37:12Z
|
出版者: | IEEE |
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. |
關聯: | 2006 IEEE International Joint Conference on Neural Networks (EI), : 603-609 |
顯示於類別: | [資訊管理學系所] 會議論文
|
文件中的檔案:
檔案 |
大小 | 格式 | 瀏覽次數 |
index.html | 0Kb | HTML | 668 | 檢視/開啟 |
|
在NCUEIR中所有的資料項目都受到原著作權保護.
|