A Fuzzy-Wavelet Method for Analyzing Non-Stationary Time Series
نویسندگان
چکیده
Fuzzy rule based systems are increasingly being used to deal with time series processes that may lack stochastic stability due to non-stationarity, multiscaling and persistent autocorrelations. Wavelet filtering can be used to deal with such phenomenon. A method for creating a fuzzy-rule base from a time series, where the first difference (returns) of the preprocessed series is used, and high frequency components have been removed, is reported. The performance of this system, trained using the fuzzy-wavelet method, is compared with a conventional fuzzy rule-based system trained on raw time series. The initial results appear encouraging in favour of the fuzzy-wavelet method.
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