Modeling time series by means of fuzzy inference systems

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

    Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

    2 Citations (Scopus)

    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.

    Original languageEnglish
    Title of host publicationMining and Control of Network Traffic by Computational Intelligence
    Pages53-85
    Number of pages33
    Volume342
    DOIs
    Publication statusPublished - 2011
    MoE publication typeA3 Part of a book or another research book

    Publication series

    NameStudies in Computational Intelligence
    Volume342
    ISSN (Print)1860-949X

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