DocumentCode :
3345990
Title :
xftsp: A tool for time series prediction by means of fuzzy inference systems
Author :
Montesino, Federico ; Lendasse, Amaury ; Barriga, Ángel
Author_Institution :
Microelectron. Inst. of Seville, Sci. Res. Council, Seville
Volume :
1
fYear :
2008
fDate :
6-8 Sept. 2008
Firstpage :
42402
Lastpage :
42407
Abstract :
A new software tool for time series prediction by means of fuzzy inference systems is reported. This tool, named xftsp, implements a novel methodology for time series prediction based on methods for automatic fuzzy systems identification and supervised learning combined with statistical methods for nonparametric residual variance estimation. xftsp is designed as a tool integrated in the Xfuzzy development environment for fuzzy systems. Experiments carried out on a number of time series benchmarks show the advantages of xftsp in terms of both accuracy and computational requirements as compared against Least-Squared Support Vector Machines, an established technique in the field of time series prediction.
Keywords :
fuzzy systems; inference mechanisms; learning (artificial intelligence); software tools; statistical analysis; support vector machines; time series; automatic fuzzy systems; fuzzy inference systems; least-squared support vector machines; nonparametric residual variance estimation; software tool; statistical methods; supervised learning; time series prediction; Evolutionary computation; Fuzzy systems; Intelligent systems; Neural networks; Predictive models; Software tools; Statistical analysis; Supervised learning; Support vector machines; Testing; Fuzzy inference; Least-squared support vector machines; Nonparametric regression; Residual variance estimation; Supervised learning; Time series prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems, 2008. IS '08. 4th International IEEE Conference
Conference_Location :
Varna
Print_ISBN :
978-1-4244-1739-1
Electronic_ISBN :
978-1-4244-1740-7
Type :
conf
DOI :
10.1109/IS.2008.4670398
Filename :
4670398
Link To Document :
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