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題名 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
作者 許毓云
Hsu, Yi-Yun
貢獻者 吳柏林
Wu, Berlin
許毓云
Hsu, Yi-Yun
關鍵詞 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
日期 1998
上傳時間 18-Sep-2009 18:28:04 (UTC+8)
摘要 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.
參考文獻 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.
描述 碩士
國立政治大學
應用數學研究所
86751005
87
資料來源 http://thesis.lib.nccu.edu.tw/record/#B2002001687
資料類型 thesis
dc.contributor.advisor 吳柏林zh_TW
dc.contributor.advisor Wu, Berlinen_US
dc.contributor.author (Authors) 許毓云zh_TW
dc.contributor.author (Authors) Hsu, Yi-Yunen_US
dc.creator (作者) 許毓云zh_TW
dc.creator (作者) Hsu, Yi-Yunen_US
dc.date (日期) 1998en_US
dc.date.accessioned 18-Sep-2009 18:28:04 (UTC+8)-
dc.date.available 18-Sep-2009 18:28:04 (UTC+8)-
dc.date.issued (上傳時間) 18-Sep-2009 18:28:04 (UTC+8)-
dc.identifier (Other Identifiers) B2002001687en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/36392-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 應用數學研究所zh_TW
dc.description (描述) 86751005zh_TW
dc.description (描述) 87zh_TW
dc.description.abstract (摘要) 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.
en_US
dc.description.tableofcontents Contents
     ABSTRACT
     LIST OF TABLE
     LIST OF FIGURE
     1. Introduction……………………………………………………………………1
     2. Genetic Modeling………………………………………………………………4
     3. Procedure of Genetic Modeling…………………………………………………9
     4. Combined Forecasting with Gene Models……………………………………11
     5. Simulation………………………………………………………………………13
     6. An Empirical Application for Exchange Rates………………………………18
     7. Conclusion………………………………………………………………………21
     References…………………………………………………………………………22
zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#B2002001687en_US
dc.subject (關鍵詞) 非線性時間數列zh_TW
dc.subject (關鍵詞) 遺傳建模zh_TW
dc.subject (關鍵詞) 主導模式zh_TW
dc.subject (關鍵詞) 隸屬度函數zh_TW
dc.subject (關鍵詞) 匯率zh_TW
dc.subject (關鍵詞) Nonlinear time seriesen_US
dc.subject (關鍵詞) Genetic modelingen_US
dc.subject (關鍵詞) Leading modelsen_US
dc.subject (關鍵詞) Membership functionen_US
dc.subject (關鍵詞) Exchange ratesen_US
dc.title (題名) 遺傳模式在匯率上分析與預測之應用zh_TW
dc.title (題名) Genetic Models and Its Application in Exchange Rates Analysis and Forecastingen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) Referenceszh_TW
dc.relation.reference (參考文獻) Andel, J.(1993). A time series model with suddenly changing parameters. Journal of Time Series Analysis, 14(2), 111-123.zh_TW
dc.relation.reference (參考文獻) Bleany,M.(1990). Some comparisons of the relative power of simple tests for Structure Change in Regression Models. Journal of Forecasting, 9, 437-444.zh_TW
dc.relation.reference (參考文獻) Chow, G, C.(1960). Testing for equality between sets of coefficients in two linear regressions. Econometrica, 28, 291-605.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) George J. Klir and Bo Yuan. (1995). Fuzzy sets and fuzzy logic. Prentice-Hall International, Inc.zh_TW
dc.relation.reference (參考文獻) Goldberg, D.E. (1989). Genetic Algorithms: In Search,Optimization,and Machine Learning. Addison-Wesley Publishing Company.zh_TW
dc.relation.reference (參考文獻) Holland , J. H. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) Koza, J. R.(1994). Genetic Programming II: Automatic Discovery of Reusable Pro grams. MIT Press,1994.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) Mitchell, M .(1996) . An Introduction to Genetic Algorithms. Cambridge , MA : MIT Press.zh_TW
dc.relation.reference (參考文獻) Nyblom, J.(1989). Testing for the constancy of parameters over time. Journal of the American Statistical Association, 844,223-230.zh_TW
dc.relation.reference (參考文獻) Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11(1), 81-92.zh_TW
dc.relation.reference (參考文獻) Barry, D. and Hartigan, J. A. (1993). A Bayesian analysis for change point problems. Journal of the American Statistical Association, 88(421), 309-319.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) Hinkey, D. V. (1971). Inference about the change point from cumulative sum test. Biometry, 26, 279-284.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) Kao, C. & Ross, S. L. (1995). A CUSUM test in the linear regression model with serially correlated disturbances. Econometric Reviews, 14(3), 331-346.zh_TW
dc.relation.reference (參考文獻) Page, E. S. (1955). A test for change in a parameter occurring at an unknown point. Biometricka, 42, 523-527.zh_TW
dc.relation.reference (參考文獻) Rukhin, A. (1997). Change-point estimation under Asymmetric loss. Statistics & Decisions, 15, 141-163.zh_TW
dc.relation.reference (參考文獻) Saatri, T., Flores, B., and Valdes, J. (1989). Detecting points of change in time series, Computers Open Research, 16, 271-293.zh_TW
dc.relation.reference (參考文獻) Tsay, R. S. (1990). Testing and modeling threshold autoregressive processes. Journal of the American Statistical Association, 84 ,231-240.zh_TW
dc.relation.reference (參考文獻) Wosley, K. J. (1986). Confidence regions and tests for a change-point in a sequence of exponential family random variables. Biometrika, 73, 91-104.zh_TW
dc.relation.reference (參考文獻) Ploberger , W. and W. Kramer. (1992). The CUSUM-test with OLS Residuals. Econometrica, 60, 271-285.zh_TW
dc.relation.reference (參考文獻) Tsay , R, S.(1991). Detecting and Modeling Non-linearity in Univariate Time Series Analysis. Statistica Sinica,1:2,431-451.zh_TW
dc.relation.reference (參考文獻) Weiss, A. A.(1986). ARCH and bilinear time series models : compares and com bination. Journal of Business and Economic Statistics,4,59-70.zh_TW
dc.relation.reference (參考文獻) Wu, B.(1994). Identification Environment and Robust Forecasting for Nonlinear Time Series. Computational Economics, 7, 37-53.zh_TW
dc.relation.reference (參考文獻) Wu, B. (1995). Model-free forecasting for nonlinear time series: with application in exchange rates. Computational Statistics and Data Analysis. 19, 433-459.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW