English  |  正體中文  |  简体中文  |  Items with full text/Total items : 6491/11663
Visitors : 25171607      Online Users : 67
RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/1868

Title: Integrating GA-based Time-scale Feature Extractions with SVMs for Stock Index Forecasting
Authors: Shian-Chang Huanga;Tung-Kuang Wub
Contributors: 企業管理學系
Keywords: Hybrid forecasting
Support vector machine
Wavelet analysis
Genetic algorithm
Time series forecasting
Date: 2008-11
Issue Date: 2010-11-15T07:47:22Z
Abstract: By integrating genetic algorithm (GA)-based optimal time-scale feature extractions with support vector machines (SVM), this study develops a novel hybrid prediction model that operates for multiple time-scale resolutions and utilizes a flexible nonparametric regressor to predict future evolutions of various stock indices. The time series of explanatory variables are decomposed using wavelet bases, and a GA is employed to extract optimal time-scale feature subsets from decomposed features. These extracted time-scale feature subsets then serve as an input for an SVM model that performs final forecasting. Compared with neural networks, pure SVMs or traditional GARCH models, the proposed model performs best. The root-mean-squared forecasting errors are significantly reduced.
Relation: Expert Systems with Applications, 35(4):2080-2088
Appears in Collections:[企業管理學系] 期刊論文

Files in This Item:

There are no files associated with this item.

All items in NCUEIR are protected by copyright, with all rights reserved.


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