English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 110097/141043 (78%)
Visitors : 46434997      Online Users : 539
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 理學院 > 應用數學系 > 學位論文 >  Item 140.119/36392
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/36392


    Title: 遺傳模式在匯率上分析與預測之應用
    Genetic Models and Its Application in Exchange Rates Analysis and Forecasting
    Authors: 許毓云
    Hsu, Yi-Yun
    Contributors: 吳柏林
    Wu, Berlin
    許毓云
    Hsu, Yi-Yun
    Keywords: 非線性時間數列
    遺傳建模
    主導模式
    隸屬度函數
    匯率
    Nonlinear time series
    Genetic modeling
    Leading models
    Membership function
    Exchange rates
    Date: 1998
    Issue Date: 2009-09-18 18:28:04 (UTC+8)
    Abstract: Abstract
    In time series analysis, we often find the trend of dynamic data changing with time. Using the traditional model fitting can`t get a good explanation for dynamic data. Therefore, many scholars developed various methods for model construction. The major drawback with most of the methods is that personal viewpoint and experience in model selection are usually influenced in them. Therefore, this paper presents a new approach on genetic-based modeling for the nonlinear time series. The research is based on the concepts of evolution theory as well as natural selection. In order to find a leading model from the nonlinear time series, we make use of the evolution rule: survival of the fittest. Through the process of genetic evolution, the AIC (Akaike information criteria) is used as the adjust function, and the membership function of the best-fitted models are calculated as performance index of chromosome. Empirical example shows that the genetic model can give an efficient explanation in analyzing Taiwan exchange rates, especially when the structure change occurs.
    Reference: References
    Andel, J.(1993). A time series model with suddenly changing parameters. Journal of Time Series Analysis, 14(2), 111-123.
    Bleany,M.(1990). Some comparisons of the relative power of simple tests for Structure Change in Regression Models. Journal of Forecasting, 9, 437-444.
    Chow, G, C.(1960). Testing for equality between sets of coefficients in two linear regressions. Econometrica, 28, 291-605.
    De Gooijer , J. G. and K. Kumar.(1992). Some recent developments in nonlinear time series modeling, testing, and forecasting. International Journal of Forecasting, 135-156.
    George J. Klir and Bo Yuan. (1995). Fuzzy sets and fuzzy logic. Prentice-Hall International, Inc.
    Goldberg, D.E. (1989). Genetic Algorithms: In Search,Optimization,and Machine Learning. Addison-Wesley Publishing Company.
    Holland , J. H. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor.
    Inclan, C. and Tiao,G.C.(1994). Use of cumulative sum of squares for retrospective detection of changes of variances. Journal of the American Statistical Association, 913-923.
    Koza, J. R.(1994). Genetic Programming II: Automatic Discovery of Reusable Pro grams. MIT Press,1994.
    Loraschi, A., Tettamani, A.,Tomassini,M.and Verda, P.(1995). Distributed genetic algorithms with a application to portfolio selection problem. Artificial Neural Networks and Genetic Algorithms, Edited by Pearson , N.C, Steele, N. C . and Al- Brett, R.F.,Springer-Verlag ,384-387.
    Mitchell, M .(1996) . An Introduction to Genetic Algorithms. Cambridge , MA : MIT Press.
    Nyblom, J.(1989). Testing for the constancy of parameters over time. Journal of the American Statistical Association, 844,223-230.
    Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11(1), 81-92.
    Barry, D. and Hartigan, J. A. (1993). A Bayesian analysis for change point problems. Journal of the American Statistical Association, 88(421), 309-319.
    Brown, R., Dubin, J., and Evans, J. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society, Ser. B, 37, 149-163.
    Hinkey, D. V. (1971). Inference about the change point from cumulative sum test. Biometry, 26, 279-284.
    Hsu D. A. (1979), Detecting shifts of parameter in gamma sequences, with applications to stock price and air traffic flow analysis. Journal of the American Statistical Association, 74, 31-40.
    Hsu, D. A. (1982), "A Bayesian robust detection of shift in the risk structure of stock market returns," Journal of the American Statistical Association, 77, 29-39.
    Inclan, C. & Tiao, G. C. (1994). Use of cumulative sums of squares for retrospective detection of changes of variance. Journal of the American Statistical Association, 89(427), 913-924.
    Kao, C. & Ross, S. L. (1995). A CUSUM test in the linear regression model with serially correlated disturbances. Econometric Reviews, 14(3), 331-346.
    Page, E. S. (1955). A test for change in a parameter occurring at an unknown point. Biometricka, 42, 523-527.
    Rukhin, A. (1997). Change-point estimation under Asymmetric loss. Statistics & Decisions, 15, 141-163.
    Saatri, T., Flores, B., and Valdes, J. (1989). Detecting points of change in time series, Computers Open Research, 16, 271-293.
    Tsay, R. S. (1990). Testing and modeling threshold autoregressive processes. Journal of the American Statistical Association, 84 ,231-240.
    Wosley, K. J. (1986). Confidence regions and tests for a change-point in a sequence of exponential family random variables. Biometrika, 73, 91-104.
    Ploberger , W. and W. Kramer. (1992). The CUSUM-test with OLS Residuals. Econometrica, 60, 271-285.
    Tsay , R, S.(1991). Detecting and Modeling Non-linearity in Univariate Time Series Analysis. Statistica Sinica,1:2,431-451.
    Weiss, A. A.(1986). ARCH and bilinear time series models : compares and com bination. Journal of Business and Economic Statistics,4,59-70.
    Wu, B.(1994). Identification Environment and Robust Forecasting for Nonlinear Time Series. Computational Economics, 7, 37-53.
    Wu, B. (1995). Model-free forecasting for nonlinear time series: with application in exchange rates. Computational Statistics and Data Analysis. 19, 433-459.
    Wu, B. and Chen, M. (1999). Use of fuzzy statistical technique in change periods detection of nonlinear time series. Applied Mathematics and Computation 99, 241-254.
    Description: 碩士
    國立政治大學
    應用數學研究所
    86751005
    87
    Source URI: http://thesis.lib.nccu.edu.tw/record/#B2002001687
    Data Type: thesis
    Appears in Collections:[應用數學系] 學位論文

    Files in This Item:

    File SizeFormat
    index.html0KbHTML2441View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    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 ©   - Feedback