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

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.