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Hybrid Intelligent Systems for Time Series Prediction

  • Oscar Castillo
  • Patricia Melin
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 63)

Abstract

We describe in this chapter a new method for the estimation of the fractal dimension of a geometrical object using fuzzy logic techniques. The fractal dimension is a mathematical concept, which measures the geometrical complexity of an object. The algorithms for estimating the fractal dimension calculate a numerical value using as data a time series for the specific problem. This numerical (crisp) value gives an idea of the complexity of the geometrical object (or time series). However, there is an underlying uncertainty in the estimation of the fractal dimension because we use only a sample of points of the object, and also because the numerical algorithms for the fractal dimension are not completely accurate. For this reason, we have proposed a new definition of the fractal dimension that incorporates the concept of a fuzzy set. This new definition can be considered a weaker definition (but more realistic) of the fractal dimension, and we have named this the “fuzzy fractal dimension”. We apply our fuzzy fractal approach to the problem of forecasting a particular time series, and compare our results to a neural network approach. The fuzzy fractal approach has some definite advantages over using neural networks, and we discuss these at the end of this chapter.

Keywords

Exchange Rate Fractal Dimension Membership Function Fuzzy System Time Series Analysis 
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

© Physica-Verlag Heidelberg 2001

Authors and Affiliations

  • Oscar Castillo
    • 1
  • Patricia Melin
    • 1
  1. 1.Department of Computer ScienceTijuana Institute of TechnologyChula VistaUSA

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