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题名: Wavelet-Based Relevance Vector Machines for Stock Index Forecasting
作者: Huang, Shian-Chang;Wu, Tung-Kuang
贡献者: 資訊管理學系
日期: 2006-07
上传时间: 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
显示于类别:[資訊管理學系所] 會議論文


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