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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/15995

Title: Combining Wavelet-Based Feature Extractions with Relevance Vector Machines for Stock Index Forecasting
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Authors: Huang, Shian-Chang;Wu, Tung-Kuang
Contributors: 資訊管理學系
Keywords: Relevance vector machine;Wavelet analysis;Time-series forecasting;Support vector machine;Neural network
Date: 2008-05
Issue Date: 2013-04-22T07:38:23Z
Publisher: Blackwell Publishing Ltd
Abstract: 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.
Relation: Expert Systems, 25(2): 133-149
Appears in Collections:[資訊管理學系所] 期刊論文

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