English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文筆數/總筆數 : 110944/141864 (78%)
造訪人次 : 47927632      線上人數 : 1353
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/36656
    請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/36656


    題名: 模糊時間數列轉折區間的認定
    Application of Fuzzy Time Series Analysis To Change Periods Detection
    作者: 莊閔傑
    貢獻者: 吳柏林
    莊閔傑
    關鍵詞: 轉折區間
    模糊轉折區間
    模糊時間數列
    景氣循環
    Structure change
    Fuzzy time series
    Fuzzy change period
    FCM measures of fuzziness
    Business cycle
    日期: 1998
    上傳時間: 2009-09-18 19:08:29 (UTC+8)
    摘要: 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.
    參考文獻: 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.
    描述: 碩士
    國立政治大學
    統計研究所
    84354003
    87
    資料來源: http://thesis.lib.nccu.edu.tw/record/#B2002001566
    資料類型: thesis
    顯示於類別:[統計學系] 學位論文

    文件中的檔案:

    沒有與此文件相關的檔案.



    在政大典藏中所有的資料項目都受到原著作權保護.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回饋