Fuzzy lagged variable selection in fuzzy time series with genetic algorithms
A new partial high order fuzzy time series forecasting model is proposed. The proposed model has high accuracy level since it does not use all lagged variables unlike other available fuzzy time series models. In the proposed model, the selection of fuzzy lagged variables is performed by using genetic algorithms. The proposed model is applied to some real life time series and very good forecasts are obtained.
variable selection fuzzy time series genetic algorithms forecasting partial high order model