Abstract: Based on the property that the fuzzy system can approximate any nonlinear continuous function in the compact supporting set, a new nonlinear combination forecasting method based on Takagi-Sugeno fuzzy rule bases is present to overcome the difficulties and drawbacks in combined modeling non-stationary time series by using linear Combination forecasting. Furthermore, the corresponding genetic algorithm is used to identify the Parameter of the fuzzy system and partitions of fuzzy subsets. Theoretical analysis and forecasting examples all show that the new technique has reinforcement learning properties and universalized capabilities. With respect to combined modeling and forecasting of economic time series in nonlinear systems, which have some uncertainties, the method is available.

Key words: economic time series, nonlinear combination modeling and forecasting, fuzzy system, genetic algorithm