National Changhua University of Education Institutional Repository : Item 987654321/17075
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 6507/11669
造访人次 : 29910954      在线人数 : 277
RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 进阶搜寻

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ir.ncue.edu.tw/ir/handle/987654321/17075

题名: Integrating Recurrent SOM with Wavelet-based Kernel Partial Least Square Regressions for Financial Forecasting
作者: Huang, Shian-Chang;Wu, Tung-Kuang
贡献者: 企業管理學系
关键词: Kernel method;Recurrent Self-Organizing Map;Support vector machine;Wavelet analysis;Hybrid model
日期: 2010-08
上传时间: 2013-07-11T09:04:43Z
出版者: Elsevier Ltd.
摘要: This study implements a novel expert system for financial forecasting. In the first stage, wavelet analysis transforms the input space of raw data to a time-scale feature space suitable for financial forecasting, and then a Recurrent Self-Organizing Map (RSOM) algorithm is used for partitioning and storing temporal context of the feature space. In the second stage, multiple kernel partial least square regressors (as local models) that best fit partitioned regions are constructed for final forecasting. Compared with neural networks, pure SVMs or traditional GARCH models, the proposed model performs best. The root-meansquared forecasting errors are significantly reduced.
關聯: Expert Systems with Applications, 37(8): 5698-5705
显示于类别:[企業管理學系] 期刊論文

文件中的档案:

档案 大小格式浏览次数
index.html0KbHTML712检视/开启


在NCUEIR中所有的数据项都受到原著作权保护.

 

DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈