National Changhua University of Education Institutional Repository : Item 987654321/15995
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题名: Combining Wavelet-Based Feature Extractions with Relevance Vector Machines for Stock Index Forecasting
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作者: Huang, Shian-Chang;Wu, Tung-Kuang
贡献者: 資訊管理學系
关键词: Relevance vector machine;Wavelet analysis;Time-series forecasting;Support vector machine;Neural network
日期: 2008-05
上传时间: 2013-04-22T07:38:23Z
出版者: Blackwell Publishing Ltd
摘要: The relevance vector machine (RVM) is a Bayesian version of the support vector machine, which with a sparse model representation has appeared to be a powerful tool for time-series forecasting. The RVM has demonstrated better performance over other methods such as neural networks or autoregressive integrated moving average based models. This study proposes a hybrid model that combines wavelet-based feature extractions with RVM models to forecast stock indices. The time series of explanatory variables are decomposed using some wavelet bases and the extracted time-scale features serve as inputs of an RVM to perform the non-parametric regression and forecasting. Compared with traditional forecasting models, our proposed method performs best. The root-mean-squared forecasting errors are significantly reduced.
關聯: Expert Systems, 25(2): 133-149
显示于类别:[資訊管理學系所] 期刊論文

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