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

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 g...

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Bibliographic Details
Published in:Automatic control and computer sciences Vol. 59; no. 1; pp. 39 - 51
Main Authors: Nguyen Thi Thu Dung, Chernenkaya, L. V.
Format: Journal Article
Language:English
Published: Moscow Pleiades Publishing 01.02.2025
Springer Nature B.V
Subjects:
Algebra
Computer Science
Control Structures and Microprogramming
Forecasting
Fuzzy logic
Fuzzy sets
Genetic algorithms
Linguistics
Neural networks
Optimization
Semantics
Time series
Variables
genetic algorithm
fuzzy type 2 model of time series forecasting
fuzzy logic type 2
hedge algebra
optimization of forecasting model
fuzzification
defuzzification type 2
ISSN:0146-4116, 1558-108X
Online Access:Get full text
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Summary: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.
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ISSN:0146-4116
1558-108X
DOI:10.3103/S014641162570004X