A hybrid-forecasting model reducing Gaussian noise based on the Gaussian support vector regression machine and chaotic particle swarm optimization

Authors:
Qi Wu;Rob Law;Edmond Wu;Jinxing Lin
Affiliations:
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200040, China and School of Hotel and Tourism Management, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong ...;School of Hotel and Tourism Management, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;School of Tourism Management, Sun Yat-sen University, No. 135, Xin'gang Xi Road, Guangzhou, Guangdong 510275, China;College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210046, China
Venue:
Information Sciences: an International Journal
Year:
2013

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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 @e-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.