Forecasting activities play an important role in our daily life. In recent years, fuzzy time series (FTS) methods were developed to\r\ndeal with forecasting problems. FTS attracted researchers because of its ability to predict the future values in some critical situations\r\nwhere most standard forecasting models are doubtfully applicable or produce bad fittings. However, some critical issues in FTS are\r\nstill open; these issues are often subjective and affect the accuracy of forecasting. In this paper, we focus on improving the accuracy\r\nof FTS forecasting methods. The new method integrates the fuzzy clustering and genetic algorithm with FTS to reduce subjectivity\r\nand improve its accuracy. In the new method, the genetic algorithm is responsible for selecting the proper model. Also, the fuzzy\r\nclustering algorithm is responsible for fuzzifying the historical data, based on its membership degrees to each cluster, and using\r\nthese memberships to defuzzify the results. This method provides better forecasting accuracy when compared with other extant\r\nresearches.
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