A Forecasting Model Fuzzy Time Series Type 2 with Hedge Algebraic and Genetic Optimization Algorithm

Abstract

In order to meet modern requirements for the development of socio-economic problems, it is necessary to develop and improve forecasting models. Existing fuzzy time series (FTS) forecasting models are built on the basis of the theory of fuzzy logic type 1, but the theory of fuzzy logic type 2 shows greater coverage of subject areas and more accurate modeling of the state of objects and systems. This is important because in reality the degree to which an element belongs to a particular set cannot be determined precisely, but only within a range. This paper proposes a fuzzy time series forecasting model based on the theory of fuzzy logic type 2 and the structure of Hedge algebra. The parameters of the proposed model are optimized using genetic algorithms. The proposed model is tested on the forecast of daily values of the Taiwan Stock Index (TAIEX) data, and the forecasting performance is assessed using the metrics RMSE, MAPE and MSE.