由於許多經濟指標的定義不明確，或是因為資料蒐集的時間不一，導致代表經濟景氣的數值，實際上即具有相當大的的不確定性。傳統的方法多不考慮這樣的模糊性,而傾向尋找一準確的模式轉折點。本文則以模糊數學的方法,運用模糊分類法以及模糊熵，訂定一個評判的準則。藉以找出一時間數列模式發生變化的轉折區間。最後以台灣經濟景氣指標為例，說明此方法可不需對資料的模式有任何事先的認知，即可得出與傳統方法相近，甚至更為合理的預測結果。

Unlike conventional change points detecting, which seeks to find a decision boundary between classes for certain structural changed time series, the purpose of this research is to investigate a new approach about fuzzy change period identification. Based on the concept of fuzzy theory, we propose a procedure for the - level of fuzzy change period detecting and prove some useful properties for a fuzzy time series. We use some numerical examples to demonstrate how these procedures can be applied. Finally, experimental results show that the proposed detecting approach for structure change of fuzzy time series is available and practical in identifying the alpha-level of fuzzy change period.

Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11(1), 81-92.
Barry, D. and J. A. Hartigan (1993). A bayesian analysis for change point problems. Journal of the American Statistical Association, 88(421), 309-319.
Bleaney, M. (1990). Some comparisions of the relative power of simple tests for structure change in regression models. Journal of Forecasting, 9, 437-444.
Broemeling, L. D. and H. Tsurumi (1987). Econometrics and structural change. Marcel Dekker Inc.
Chow, G. C. (1960), Testing for equality between sets of coefficients in two linear regressions. Econometrica, 28, pp. 591-605.
Custem, B. V. and I. Gath (1993). Detection of outliers and robust estimation using fuzzy clustering. Computational Statistics and Data Analysis, 15, 47-61.
Hathaway, R. J. and J. C. Bezdek (1993). Switching regression models and fuzzy clustering. IEEE Transactions on fuzzy systems, 1(3), 195-204.
Heshmaty, B. A. Kandel (1985). Fuzzy linear regression and its applications to forecasting in uncertain environment. Fuzzy Sets and System, 15, 159-191.
Klir, G. J. and T. A. Folger, (1988). Fuzzy Sets, Uncertainty, and Information. Englewood Cliffs, NJ：Prentice Hall.
Lin, C. F. and T. Ter svirta (1994), Testing the constancy of regression parameters against continuous structural change. Journal of Econometrics, 62, 211-228.
Nyblom, J. (1989). Testing for the Constance of Parameters over Time. Journal of the American Statistical Association, 84, 223-230.
Oh, S. B., Kim, W. and Lee J. K. (1990). An approach to causal modeling in fuzzy environment and its application. Fuzzy Sets and System, 35, 43-55.
Ploberger, W., W. Kramer, and K. Kontrus (1989), A new test for structural stability in the linear regression model. Journal of Econometrics, 40, 307-318.
Song, Q. and B. S. Chissom (1993a). Fuzzy Time Series and its Models, Fuzzy Sets and Systems, 54, 267-277.
Song, Q. and B. S. Chissom (1993b). Forecasting Enrollments With Fuzzy Time Series-Part I, Fuzzy Sets and Systems, 54, 1-9.
Tsay, R. S. (1988). Outliers, level shifts, and variance changes in time series. Journal of forecasting, 7, 1-20.
Wu, B. (1994). On fuzzy identification of nonlinear time series. Technique Reports.
Yoshinari, Y., W. Pedrycz, and K. Hirota (1993). Construction of fuzzy models through clustering techniques. Fuzzy Sets and Systems, 54, 157-165.
Zadeh, L. A. (1965), Fuzzy Sets. Information and Control, 8, 338-353.
Zimmermann, H. J. (1991), Fuzzy Set Theory and Its Applications. Boston: Kluwer Academi.
Mu-Song Chen and Shinn-Wei Wang (1999), Fuzzy clustering analysis for optimizing fuzzy membership functions. Fuzzy Set and Systems, 103, 239-254
Tai Wai Cheng, Dmitry B. Goldgof, Lawrence O. Hall (1999), Faster fuzzy Clustering, Fuzzy Set and Systems, 93, 49-56.
A.F. Gomez-Skarmeta, M. Delgado, M.A. Vila (1999), About the use of fuzzy clustering techniques for fuzzy model identification, Fuzzy Set and systems, 106, 179-188.
Toly Chen, Mao-Jiun J. Wang (1999), Forcasting methods using fuzzy concepts, Fuzzy Set and Ssystems, 105, 339-352.