A NEW FUZZY TIME SERIES ANALYSIS APPROACH BY USING DIFFERENTIAL EVOLUTION ALGORITHM AND CHRONOLOGICALLY-DETERMINED WEIGHTS

Fuzzy time series approaches, which do not require the strict assumptions of traditional time series approaches, generally consist of three stages. These stages are called as the fuzzification of crisp time series observations, the identification of fuzzy relationships and the defuzzification, respectively. All of these stages play an important role on the forecasting performance of the model. By this study we want to contribute to the stage of fuzzification so that the interval length is determined by using the differential evolution algorithm and also we take into account chronological-determined weights in the stage of defuzzification.