An improved fuzzy time series forecasting model using the differential evolution algorithm

Article type: Research Article

Authors: Thao, Nguyen-Trang^{a; b; *}

Affiliations: [a] Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam | [b] Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Correspondence: [*] Corresponding author. Nguyen-Trang Thao. E-mail: [email protected].

Abstract: Fuzzy time series modeling has recently become an interesting topic to study. Among fuzzy time series models, the Abbasov-Mamedova (AM) model has advantages over the others because it can forecast the value that is outside the min-max range of the original data. However, the performance of the AM model strongly depends on three parameters that are user-defined. In previous studies, the optimal parameters of the fuzzy time series models have been identified with a global optimization method. Surprisingly, optimizing the parameters of the Abbasov and Mamedova model has not been solved in spite of its advantages over the others. This paper presents a new approach to improve the performance of AM model based on the evolutionary algorithm. Particularly, the objective function is calculated as the Mean absolute percentage error which will be minimized using the differential evolution (DE) algorithm. The experiments on Azerbaijan’s population, Vietnam’s GDP and rice production demonstrate the feasibility and applicability of the proposed methods.

Keywords: fuzzy time series, differential evolution, optimization, forecast, MAPE

DOI: 10.3233/JIFS-18636

Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 2, pp. 1727-1741, 2019