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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\u2019s population, Vietnam\u2019s GDP and rice production demonstrate the feasibility and applicability of the proposed methods.<\/jats:p>","DOI":"10.3233\/jifs-18636","type":"journal-article","created":{"date-parts":[[2018,9,4]],"date-time":"2018-09-04T14:52:12Z","timestamp":1536072732000},"page":"1727-1741","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["An improved fuzzy time series forecasting model using the differential evolution algorithm"],"prefix":"10.1177","volume":"36","author":[{"given":"Nguyen-Trang","family":"Thao","sequence":"first","affiliation":[{"name":"Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam"},{"name":"Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2018,8,28]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"545","volume-title":"8th Symposion on Information Technology in Urban and Spatial Planning","author":"Abbasov A.M.","year":"2003","unstructured":"AbbasovA.M., MamedovaM.H., Application of fuzzy time series to population forecasting, in: 8th Symposion on Information Technology in Urban and Spatial Planning, Vienna University of Technology, 2003, pp. 545\u2013552."},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.02.149"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2012.05.002"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-120738"},{"issue":"1","key":"e_1_3_1_6_2","first-page":"2073","article-title":"Differential evolution with DEoptim","volume":"3","author":"Ardia D.","year":"2011","unstructured":"ArdiaD., BoudtK., CarlP., MullenK.M. and PetersonB.G., Differential evolution with DEoptim, R Journal 3(1) (2011), ISSN 2073\u20134859.","journal-title":"R Journal"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-014-0529-x"},{"key":"e_1_3_1_8_2","volume-title":"Holden-Day Series in Time Series Analysis","author":"Box G.E.P.","year":"1976","unstructured":"BoxG.E.P., JenkinsG.M., Time series analysis, Forecasting and control, in: Holden-Day Series in Time Series Analysis, Revised ed., San Francisco: Holden-Day, 1976, (1976)."},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(95)00220-0"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1080\/019697202753306479"},{"issue":"3","key":"e_1_3_1_11_2","first-page":"234","article-title":"A new method to forecast enrollments using fuzzy time series","volume":"2","author":"Chen S.-M.","year":"2004","unstructured":"ChenS.-M. and HsuC.-C., A new method to forecast enrollments using fuzzy time series, International Journal of Applied Science and Engineering 2(3) (2004), 234\u2013244, ISSN 1727-2394.","journal-title":"International Journal of Applied Science and Engineering"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"e_1_3_1_13_2","first-page":"1955","author":"Dacal-Nieto A.","year":"2009","unstructured":"Dacal-NietoA., V\u00e1zquez-Fern\u00e1ndezE., FormellaA., MartinF., Torres-GuijarroS., Gonz\u00e1lez-JorgeH., A genetic algorithm approach for feature selection in potatoes classification by computer vision, 2009, pp. 1955\u20131960. 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