PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Fuzzy Inference Based Autoregressors for Time Series Prediction Using Nonparametric Residual Variance Estimation
Federico Montesino Pouzols, Amaury Lendasse and Angel Barriga Barros
In: 17th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE2008), IEEE World Congress on Computational Intelligence, June 2008, Hong Kong.

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4799
Deposited By:Amaury Lendasse
Deposited On:24 March 2009