바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

  • P-ISSN1226-0657
  • E-ISSN2287-6081
  • KCI

FINANCIAL TIME SERIES FORECASTING USING FUZZY REARRANGED INTERVALS

Journal of the Korean Society of Mathematical Education Series B: The Pure and Applied Mathematics / Journal of the Korean Society of Mathematical Education Series B: The Pure and Applied Mathematics, (P)1226-0657; (E)2287-6081
2012, v.19 no.1, pp.7-21
https://doi.org/10.7468/jksmeb.2012.19.1.7
Jung, Hye-Young
Yoon, Jin-Hee
Choi, Seung-Hoe

Abstract

The fuzzy time series is introduced by Song and Chissom([8]) to construct a pattern for time series with vague or linguistic value. Many methods using the interval and fuzzy logical relationship related with historical data have been suggested to enhance the forecasting accuracy. But they do not fully reflect the fluctuation of historical data. Therefore, we propose the interval rearranged method to reflect the fluctuation of historical data and to improve the forecasting accuracy of fuzzy time series. Using the well-known enrollment, the proposed method is discussed and the forecasting accuracy is evaluated. Empirical studies show that the proposed method in forecasting accuracy is superior to existing methods and it fully reflects the fluctuation of historical data.

keywords
fuzzy time series, forecasting, rearranged interval method, fluctuation

Reference

1.

(1996). Forecasting enrollments hased on fuzzy time series. Fuzzy Sets and Systems, 81, 311-319. 10.1016/0165-0114(95)00220-0.

2.

(2008). Fuzzy time- series based on adaptive expectation model for TAIEX forecasting. Expert Systems with Applications, 34, 1126-1132. 10.1016/j.eswa.2006.12.021.

3.

(2001). Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets and Systems, 123, 155-162.

4.

(2001). Heuristic models of fuzzy time series for forecasting. Fuzzy Sets and System, 123, 369-386. 10.1016/S0165-0114(00)00093-2.

5.

(2006). Handling forecasting problems based on two-factors high-order fuzzy time series. IEEE Transactions on Puzzy Systems, 14, 468-477. 10.1109/TFUZZ.2006.876367.

6.

(2008). Temperature prediction and TAIFEX forecasting based on high-order fuzzy logical relationships and genetic simulated annealing techniques. Expert Systems with Applications, 34, 328-336. 10.1016/j.eswa.2006.09.007.

7.

(2007). An improved fuzzy times forecasting method using trapezoidal fuzzy numbers. Fuzzy Optimization Decision Making, 6, 63-80. 10.1007/s10700-006-0025-9.

8.

(1993). Forecasting enrollments with fuzzy time series- part 1. Fuzzy Sets and Systems, 54, 1-10. 10.1016/0165-0114(93)90355-L.

9.

(1993). Forecasting enrollments with fuzzy time series-part 11. Fuzzy Sets and Systems, 62, 1-8.

Journal of the Korean Society of Mathematical Education Series B: The Pure and Applied Mathematics