Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23222
Title: A hybrid-forecasting model reducing Gaussian noise based on the Gaussian support vector regression machine and chaotic particle swarm optimization
Authors: Wu, Q
Law, R 
Wu, E
Lin, J
Keywords: Support vector regression machine
Gaussian loss function
Particle swarm optimization
Chaotic mapping
Forecast
Issue Date: 2013
Publisher: Elsevier
Source: Information sciences, 2013, v. 238, p. 96-110 How to cite?
Journal: Information sciences 
Abstract: In this paper, the relationship between Gaussian noise and the loss function of the support vector regression machine (SVRM) is analyzed, and then a Gaussian loss function proposed to reduce the effect of such noise on the regression estimates. Since the epsilon-insensitive loss function cannot reduce noise, a novel support vector regression machine: g-SVRM, is proposed, then a chaotic particle swarm optimization (CPSO) algorithm developed to estimate its unknown parameters. Finally, a hybrid-forecasting model combining g-SVRM with the CPSO is proposed to forecast a multi-dimensional time series. The results of two experiments demonstrate the feasibility of this approach.
URI: http://hdl.handle.net/10397/23222
ISSN: 0020-0255
EISSN: 1872-6291
DOI: 10.1016/j.ins.2013.02.017
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