Fuzzy time series based on defining interval length with Imperialist Competitive Algorithm

Author

Zarandi, M. H Fazel ; Molladavoudi, A. ; Hemmati, A.

Author_Institution

Ind. Eng. Dept., Amirkabir Univ. of Technol. (Polytech.), Tehran, Iran

fYear

2010

fDate

12-14 July 2010

Firstpage

1

Lastpage

7

Abstract

Determining interval length in fuzzy time series has been one of the main concerns of many researchers in this area. Since an interval length has a continuous nature, in this paper, a novel metaheuristic algorithm (ICA), Imperialist Competitive Algorithm, is implemented. ICA can determine accurate interval length and it directly leads to results of fuzzy time series. For checking the validity of proposed model and algorithm, three well known bench mark problems, Daily Temperature in Taipei (Taiwan (1996), TAIFEX series (1996), and Alabama University Enrollment, is used. The results show that the proposed model can reduce both MSE and MAPE in all above mentioned bench mark problems.

Keywords

competitive algorithms; forecasting theory; fuzzy set theory; mean square error methods; prediction theory; time series; Alabama University Enrollment; MSE; TAIFEX series; Taipei; bench mark problems; daily temperature; fuzzy time series; imperialist competitive algorithm; interval length; mean square error; metaheuristic algorithm; Arithmetic; Fuzzy logic; Fuzzy set theory; Fuzzy sets; Humans; Independent component analysis; Industrial engineering; Optimal control; Probability distribution; Temperature; Defuzzification; Fuzzy time series; ICA; fuzzy logical relationships; interval length;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society (NAFIPS), 2010 Annual Meeting of the North American

Conference_Location

Toronto, ON

Print_ISBN

978-1-4244-7859-0

Electronic_ISBN

978-1-4244-7857-6

Type

conf

DOI

10.1109/NAFIPS.2010.5548295

Filename

5548295