A forecasting model fuzzy time series type 2 with hedge algebraic and general optimization algorithm

Nguyen Thi Thu Dung, L.V. Chernenkaya

Abstract


In order to keep with the evolution of socioeconomic problems, the development of forecasting models increasingly needs improvement. Existing fuzzy time series (FTS) forecasting models are based on the first type fuzzy  logic theory, but the second type fuzzy logic theory shows greater coverage and more accurate modeling of reality in many cases. This is suitable because in reality, the degree of membership of an element to a set cannot be determined specifically, but only within a range. In this paper, a fuzzy time series forecasting model is proposed based on type two fuzzy logic theory and Hedge algebra structure. The parameters of the proposed model are optimized using genetic algorithm. The proposed model is tested by forecasting the daily values of TAIEX data and the forecasting performance is evaluated by RMSE, MAPE and MSE metrics.

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