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Behavior of similarity-based neuro-fuzzy networks and evolutionary algorithms in time series model mining

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3 Author(s)
Valdes, J.J. ; Inst. for Inf. Technol., Nat. Res. Council of Canada, Ottawa, Ont., Canada ; Barton, A. ; Paul, R.

This paper presents the first in a series of experiments to study the behavior of a hybrid technique for model discovery in multivariate time series using similarity based neurofuzzy neural networks and genetic algorithms. This method discovers dependency patterns relating future values of a target series with past values of all examined series, and then constructs a prediction function. It accepts a mixture of numeric and non-numeric variables, fuzzy information, and missing values. Experiments were made changing parameters controlling the algorithm from the point of view of: i) the neuro-fuzzy network, ii) the genetic algorithm, and iii) the parallel implementation. Experimental results show that the method is fast, robust and effectively discovers relevant interdependencies.

Published in:

Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on  (Volume:4 )

Date of Conference:

18-22 Nov. 2002