An Improved Fuzzy Time Series Model For Forecasting

نویسندگان

  • Ashraf K. Abd-Elaal
  • Hesham A. Hefny
  • Ashraf H. Abd-Elwahab
چکیده

Researchers introduce in this paper, an efficient fuzzy time series forecasting model based on fuzzy clustering to handle forecasting problems and improving forecasting accuracy. Each value (observation) is represented by a fuzzy set. The transition between consecutive values is taken into account in order to model the time series data. Proposed model employed eight main steps in time-invariant fuzzy time-series and time-variant fuzzy time series models to increase the performance of the proposed fuzzy time series model. The method of FCMI is integrated in the processes of fuzzy time series to partition datasets. The proposed model has been implemented to forecast the world production of iron and steel and the enrollments of the University of Alabama. The proposed model provide higher accuracy in forecasting. Our results show that this approach can lead to satisfactory performance for fuzzy time series

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تاریخ انتشار 2010