The experimental results show that the proposed method is better than the others on the accuracy of forecasting. It is simple and flexible in applying this method because we can determine the parameters of HA for reasonable intervals. | Journal of Science and Technology 54 (5) (2016) 571-583 DOI: THE PARTITIONING METHOD BASED ON HEDGE ALGEBRAS FOR FUZZY TIME SERIES FORECASTING Hoang Tung1, *, Nguyen Dinh Thuan1, Vu Minh Loc2 1 2 University of Information Technology, Linh Trung Ward, Thu Duc Dist, Ho Chi Minh City Ba Ria-Vung Tau University, 80 Truong Cong Dinh Str, Vung Tau City, Ba Ria-Vung Tau Pro * Email: tung_k51e@ Received: 27 October 2015; Accepted for publication: 19 June 2016 ABSTRACT In recent years, many partitioning methods have been proposed for fuzzy time series, because they strongly affect to forecasting results. In this paper, we present a novel partitioning method based on hedge algebras (HA). The experimental results show that the proposed method is better than the others on the accuracy of forecasting. It is simple and flexible in applying this method because we can determine the parameters of HA for reasonable intervals. Keywords: fuzzy time series, forecasting time series, reasonable intervals, hedge algebras. 1. INTRODUCTION In the first research on the fuzzy time series in 1993, Song and Chissom [1] proposed a method (S&C) that used fuzzy time series to forecast time series. According to that, C(t) is the conventional time series that needs to be forecasted, this one can be forecasted by converting into fuzzy time series F(t). After that, the forecasting result on F(t) is defuzzified to become the forecasting result on C(t). So, F(t) is a qualitative view about C(t). Because of this, we offer a convention by giving the collection of all historical values of F(t) to be C(t) and the values of F(t) to be the linguistic terms that are used to qualitatively describe the values of C(t). The method S&C can be summarized in seven steps: (1) Determining U which is the universe of discourse of F(t), (2) Partitioning U into a collection of intervals, (3) Determining the collection of linguistic terms used to quanlitatively describe the .
