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Fuzzy Autoregressive Rules: Towards Linguistic Time Series Modeling

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  • Jose Luis Aznarte
  • Jesus Alcala-Fdez
  • Antonio Arauzo
  • Jose Manuel Benitez
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    Abstract

    Fuzzy rule-based models, a key element in soft computing (SC), have arisen as an alternative for time series analysis and modeling. One difference with preexisting models is their interpretability in terms of human language. Their interactions with other components have also contributed to a huge development in their identification and estimation procedures. In this article, we present fuzzy rule-based models, their links with some regime-switching autoregressive models, and how the use of soft computing concepts can help the practitioner to solve and gain a deeper insight into a given problem. An example on a realized volatility series is presented to show the forecasting abilities of a fuzzy rule-based model.

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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

    Volume (Year): 30 (2011)
    Issue (Month): 6 ()
    Pages: 646-668

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    Handle: RePEc:taf:emetrv:v:30:y:2011:i:6:p:646-668

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    Related research

    Keywords: Fuzzy models; Regime-switching models; Soft computing; Time series; Volatility;

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