Behaviour of Similarity-Based Neuro-Fuzzy Networks and Evolutionary Algorithms in Time Series Model Mining

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TypeArticle
ConferenceProceedings of the 9th International Conference on Neural Information Processing (ICONIP'02), November 18-22, 2002., Orchid Country Club, Singapore
AbstractThis 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.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number44954
NPARC number5765378
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Record identifier7d5e1fef-04ab-421e-8097-c66e48798f31
Record created2009-03-29
Record modified2016-05-09
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