NDLI: Using PSO algorithm to evolve an optimum input subset for a SVM in time series forecasting

Content Provider	IEEE Xplore Digital Library
Author	Chunkai Zhang Hong Hu
Copyright Year	2005
Description	Author affiliation: Dept. of Mech. Eng. & Autom., Harbin Inst. of Technol., Shenzhen, China (Chunkai Zhang; Hong Hu)
Abstract	Using particle swarm optimization (PSO) algorithm to evolve an optimum input subset for a SVM is proposed Binary PSO algorithm is employed in feature selection, in which each particle represented as a binary vector corresponds to a candidate input subset. A swarm of particles flies through the input set space for targeting the optimal subset. In order to evaluate the reasonable fitness of each input subset, PSO algorithm is used to adoptively evolve SVM to obtain the best performance of network, in which each particle represented as a real vector corresponds to the candidate kernel parameters of SVM. This method has been applied in a real financial time series forecasting, the results show that it has better performance of generalization, and higher rate of convergence.
Starting Page	3793
Ending Page	3796
File Size	275685
Page Count	4
File Format	PDF
ISBN	0780392981
DOI	10.1109/ICSMC.2005.1571737
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Kernel Technology forecasting Mechanical engineering Automation Particle swarm optimization Convergence Statistical learning Risk management Upper bound time series forecasting PSO algorithm optimum input
Content Type	Text
Resource Type	Article

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