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Modeling Time Series by Means of Fuzzy Inference Systems

  • Federico Montesino Pouzols
  • Diego R. Lopez
  • Angel Barriga Barros
Part of the Studies in Computational Intelligence book series (SCI, volume 342)

Abstract

In this chapter, we focus on long-term modeling and prediction of univariate nonlinear time series. First, a method for long-term time series prediction by means of fuzzy inference systems combined with residual variance estimation techniques is developed and validated through a number of time series prediction benchmarks. This method provides an automatic means of modeling and predicting network traffic load, and can thus be classified as a method for predictive data mining. Although the primary focus in this section is to develop a methodology for building simple and thus interpretable fuzzy inference systems, it will be shown that they also outperform some of the most accurate and commonly used techniques in the field of time series prediction.

Keywords

Membership Function Fuzzy System Fuzzy Model Fuzzy Inference System Modeling Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Federico Montesino Pouzols
    • Diego R. Lopez
      • Angel Barriga Barros

        There are no affiliations available

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