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Please use this identifier to cite or link to this item:
http://ir.ncue.edu.tw/ir/handle/987654321/15958
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| Title: | Wavelet-Based Relevance Vector Machines for Stock Index Forecasting |
| Authors: | Huang, Shian-Chang;Wu, Tung-Kuang |
| Contributors: | 資訊管理學系 |
| Date: | 2006-07
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| Issue Date: | 2013-04-22T07:37:12Z
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| Publisher: | IEEE |
| Abstract: | 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. |
| Relation: | 2006 IEEE International Joint Conference on Neural Networks (EI), : 603-609 |
| Appears in Collections: | [資訊管理學系所] 會議論文
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