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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/15958

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
Issue Date: 2013-04-22T07:37:12Z
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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