Title :
Modeling time series volatility using fuzzy rule systems
Author :
Bykhanov, K.V. ; Popov, A.A.
Abstract :
Summary form only given. A special class of volatility models based on Takagi-Sugeno fuzzy rules is presented in the paper. The corresponding identification problem is stated and techniques for constructing and estimating fuzzy rules by observation data are thoroughly described. A comparison between models of proposed class and traditional ARCH-/GARCH-models is made for a sample data produced by a non-linear model. A question of using fuzzy rule systems for modeling time-varying volatility of real-life time series is briefly discussed.
Keywords :
fuzzy set theory; time series; ARCH-models; GARCH-models; Takagi-Sugeno fuzzy rule systems; identification problem; time series volatility; Analysis of variance; Computer simulation; Fuzzy systems; Gaussian distribution; Logistics; Power system modeling; Statistical analysis; Statistical distributions; Takagi-Sugeno model; Testing;
Conference_Titel :
Electronic Instrument Engineering, 2008. APEIE 2008. 9th International Conference on Actual Problems of
Conference_Location :
Novosibirsk
Print_ISBN :
978-1-4244-2825-0
DOI :
10.1109/APEIE.2008.4897171
