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Modeling Time Series by Means of Fuzzy Inference Systems

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Mining and Control of Network Traffic by Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((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.

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Authors
  1. Federico Montesino Pouzols
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  2. Diego R. Lopez
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  3. Angel Barriga Barros
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Pouzols, F.M., Lopez, D.R., Barros, A.B. (2011). Modeling Time Series by Means of Fuzzy Inference Systems. In: Mining and Control of Network Traffic by Computational Intelligence. Studies in Computational Intelligence, vol 342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18084-2_2

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  • DOI: https://doi.org/10.1007/978-3-642-18084-2_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-18083-5

  • Online ISBN: 978-3-642-18084-2

  • eBook Packages: EngineeringEngineering (R0)

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