• 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