Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Time Series Using Fuzzy Logic

삼각퍼지수를 이용한 시계열모형

  • 정혜영 (연세대학교 수학과) ;
  • 최승회 (한국항공대학교 인문자연과학부)
  • Published : 2008.07.16
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Abstract

In this paper we introduce a time series model using the triangle fuzzy numbers in order to construct a statistical relation for the data which is a sequence of observations which are ordered in time. To estimate the proposed fuzzy model we split of a universal set includes all observation into closed intervals and determine a number and length of the closed interval by the frequency of events belong to the interval. Also we forecast the data by using a difference between observations when the fuzzified numbers equal at successive times. To investigate the efficiency of the proposed model we compare the ordinal and the fuzzy time series model using examples.

본 논문은 시간의 흐름에 따라 일정한 간격으로 관측된 시계열자료에 대한 통계적인 관계를 추정하기 위하여 삼각퍼지수를 이용한 퍼지시계열모형을 소개한다. 모든 관측치를 포함하는 전체집합을 분할하는 구간을 자료의 빈도수에 따라 결정하고 연속되는 두 시점에서 퍼지수가 일치하는 경우에는 관측된 자료의 차에 대한 정보를 이용하여 제안된 퍼지시계열모형을 추정한다. 예제를 이용하여 제안된 퍼지시계열모형의 정확성을 일반적인 시계열모형과 여러 가지 방법으로 추정된 퍼지시계열모형과 비교한다.

Keywords

References

  1. Chen, S. M. (1996). Forecasting enrollments based on fuzzy time series, Fuzzy Sets and Systems, 81, 311-319 https://doi.org/10.1016/0165-0114(95)00220-0
  2. Cheng, C. H., Chen, T. L. and Chiang, C. H. (2006). Trend-weighted fuzzy time series model for TAIEX forecasting, Lecture Notes in Computer Science, 4234, 469-477 https://doi.org/10.1007/11893295_52
  3. Huarng, K. (2001a). Effective lengths of intervals to improve forecasting in fuzzy time series, Fuzzy Sets and Systems, 123, 387-394 https://doi.org/10.1016/S0165-0114(00)00057-9
  4. Huarng, K. (2001b). Heuristic models of fuzzy time series for forecasting, Fuzzy Sets and Systems, 123, 369-386 https://doi.org/10.1016/S0165-0114(00)00093-2
  5. Huarng, K. and Yu, H. K. (2003). An N-th order heuristic fuzzy time series model for TAIEX forecasting, International Journal of Fuzzy Systems, 5, 247-253
  6. Hwang, J. R., Che, S. M. and Lee, C. H. (1998). Handling forecasting problems using fuzzy time series, Fuzzy Sets and Systems, 100, 217-218 https://doi.org/10.1016/S0165-0114(97)00121-8
  7. Lee, H. S. and Chou, M. T. (2004). Fuzzy forecasting based on fuzzy time series, International Journal of Computer Mathematics, 81, 781-789 https://doi.org/10.1080/00207160410001712288
  8. Liu, H. T. (2007). An improved fuzzy time series forecasting method using trapezoidal fuzzy numbers, Fuzzy Optimization Decision Making, 6, 63-80 https://doi.org/10.1007/s10700-006-0025-9
  9. Shah, M. (2007). Fuzzy time series: A realistic method to forecast gross domestic capital of India, Analysis and Design of Intelligent Systems using Soft Computing Techniques, 41, 255-264 https://doi.org/10.1007/978-3-540-72432-2_26
  10. Song, Q. and Chissom, B. S. (1993a). Forecasting enrollments with fuzzy time series-part I, Fuzzy Sets and Systems, 54, 1-9 https://doi.org/10.1016/0165-0114(93)90355-L
  11. Song, Q. and Chissom, B. S. (1993b). Fuzzy time series and its models, Fuzzy Sets and Systems, 54, 269-277 https://doi.org/10.1016/0165-0114(93)90372-O
  12. Song, Q. and Chissom, B. S. (1994). Forecasting enrollments with fuzzy time series-part II, Fuzzy Sets and Systems, 62, 1-8 https://doi.org/10.1016/0165-0114(94)90067-1
  13. Sullivan, J. (1994). A comparison of fuzzy forecasting and Markov modeling, Ph. D. Dissertation, The University of Alabama
  14. Tseng, F. M., Tzeng, G. H., Yu, H. C. and Yuan, B. J. C. (2001). Fuzzy ARIMA model for forecasting the foreign exchange market, Fuzzy sets and Systems, 118, 9-19 https://doi.org/10.1016/S0165-0114(98)00286-3
  15. Yu, H. K. (2005). Weighted fuzzy time series models for TAIEX forecasting, Physica A: Statistical Mechanics and Its Applications, 349, 609-624 https://doi.org/10.1016/j.physa.2004.11.006