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    Title: 非線型時間序列之穩健預測
    Robust Forecasting For Nonlinear Time Series
    Authors: 劉勇杉
    Liu, Yung Shan
    Contributors: 吳柏林
    Wu, Berlin
    劉勇杉
    Liu, Yung Shan
    Keywords: 神經網路
    雙線型模式
    倒傳遞網路
    匯率
    neural networks
    bilinear model
    backpropagation
    exchange rates
    Date: 1993
    Issue Date: 2016-04-29 16:32:31 (UTC+8)
    Description.abstract: 由於時間序列在不同範疇的廣泛應用，許多實證結果已明白指出時間序列
    With rapid development at the study of time series, the
    Reference: [1] Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis: Fore-casting and Control. 2nd ed. San Francisco : Holden-Day.
    [2] Brockett,R.W.(1976).Volterra series and geometric control theory. Au-tomatica, 12. 167-176.
    [3] Chan, W.S. and Tong, H. (1986). On test for non-linearity in time series analysis. J. Forecasting, 5, 217-28.
    [4] Cynbento, G., (1989). Approximation by superposition of a sigmoidal function, Mathematics of Control, Signals and Systems, 2, 303-314.
    [5] De Gooijer, J.G. and Kumar, K.(1992). Some recent developments in nonlinear time series modelling, testing and forecasting. International Journal of Forecasting, 8, 135-156.
    [6] Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of U.K. inflation. Econometrica, 50, 987-1008.
    [7] Funahashi, K. I., (1989). On the approximate of continuous mappings by neural networks, Neural Networks, 2, 183-192.
    [8] Granger, C.W.J. and Anderson, A. P. (1978). An Introduction to Bi-linear Time Series Models. Vandenhoeck and Ruprech, Gottingen.
    [9] Granger, C.W.J. (1991). Developments in the nonlinear analysis of economic series. Scand. J. Of Economics. 93(2), 263-276.
    [10] Grosberg, S. (1988). Studics of Mind and Brain: Neural Principles of Learning, Perception, Development, Cognition and Motor Control. Boston, MA: Reidel.
    [11] Guegan, D. and Pham, T.D. (1992). Power of the score test against bilinear time series models. Statistica Sinica, Vol. 2, 1, 157-169.
    [12] Hecht-Nielsen, R., (1989). Neurocomputing, IEEE Spectrum, March, 36-41.
    [13] Hinich, M. (1982). Testing for Gaussianity and linearity of a stationary time series. J. Time series Analysis, Vol.3, No.3, 169-76.
    [14] Kolen, J. F. and Goel, A. K. (1991). Learning in parallel distributed processing networks: computational complexity and information con-tent. IEEE Transactions on Systems, Man, and Cybernetics, 21, 2, 359-367.
    [15] Kosko, B. (1992). Neural Networks for Signal Processing, Prentice Hall, Englewood Cliffs, NJ.
    [16] Lapedes, A., and Farber, R., (1988). How Neural Nets Work. The-oretical Division. Los Alamos National Laboratory Los Alamos, NM 87545.
    [17] Luukkonen, R., Saikkonen P. and Terasvirta, T. (1988). Testing lin-earity against smooth transition autocorrelation models. Biometrica, 75, 491-500.
    [18] McKenzie, E. (1985). Some simple models for discrete variate time series. In Time Series Analysis in Water Resources. (ed. K. W. Hipel), 645-650, AM. Water Res. Assoc.
    [19] Priestley, M. B. (1980). State-dependent models: a general approach to nonlinear time series. J. Time Series Anal. 1, 47-71.
    [20] Saikkonen, P. and Luukkonen, K. (1988). Lagrange multiplier test for testing non-linearities in time series models. Scand. J. of Statistics, 15, 55-68.
    [21] Saikkonen, P. and Luukkonen, K. (1991). Power properties of a time series linearity test against some simple bilinear alternatives. Statistica Sinica, Vol. 1, 2, 453-464.
    [22] Subba Rao, T. and Gabr, M. M. (1984). An Introduction to Bispectral Analysis and Bilinear Time Series Models. Lecture Notes in statistics, Springer- Verlag, London.
    [23] Tjoostheim, D.(1986). Some doubly stochastic time series models J. Time Ser. Analysis, 7, 51-72.
    [24] Tong, H. And Lim, K. S. (1980). Threshold autoregression, limit cycles and cyclical data. J. Roy. Statist. Soc. Ser. B, 42, 245-292.
    [25] Tsay, R. S. (1989). Testing and modeling threshold autoregressive pro-cesses. Journal of the American Statistical Association, 84, 231-240.
    [26] Tsay, R. S. (1991). Detecting and modeling nonlinearity in univariate time series analysis. Statistica Sinica, Vol. 1, 2, 431-51.
    [27] Weiss, A. A. (1986). ARCH and bilinear time series models: compari-son and combination. J. Business Economic Statistics. Vol. 4, No. 1, 59-70.
    [28] Wu, B., Liou, W. And Chen, Y. (1992). Robust forecasting for the stochastic models and chaotic models. J. Chinese Statist. Assoc. Vol.30, No. 2, 169-189.
    [29] Wu, B. And Shih, N. (1992). On the identification problem for bilinear time series models. J. Statist. Comput. Simul. Vol.43. 129-161.
    Description: 碩士
    國立政治大學
    應用數學系
    80155004
    Source URI: http://thesis.lib.nccu.edu.tw/record/#B2002004238
    Data Type: thesis
    DCField Value Language
    dc.contributor.advisor 吳柏林zh_TW
    dc.contributor.advisor Wu, Berlinen_US
    dc.contributor.author 劉勇杉zh_TW
    dc.contributor.author Liu, Yung Shanen_US
    dc.creator (Authors) 劉勇杉zh_TW
    dc.creator (Authors) Liu, Yung Shanen_US
    dc.date (Date) 1993en_US
    dc.date.accessioned 2016-04-29 16:32:31 (UTC+8)-
    dc.date.available 2016-04-29 16:32:31 (UTC+8)-
    dc.date.issued (Issue Date) 2016-04-29 16:32:31 (UTC+8)-
    dc.identifier (Other Identifiers) B2002004238en_US
    dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/88741-
    dc.description (Description) 碩士zh_TW
    dc.description (Description) 國立政治大學zh_TW
    dc.description (Description) 應用數學系zh_TW
    dc.description (Description) 80155004zh_TW
    dc.description.abstract (Abstract) 由於時間序列在不同範疇的廣泛應用，許多實證結果已明白指出時間序列zh_TW
    dc.description.abstract (Abstract) With rapid development at the study of time series, theen_US
    dc.description.tableofcontents 1 Introduction 1
    2 Neural Networks and Model-free Forecast 4
    2.1 Motivation for forecasting nonlinear time series..........................................4
    2.2 Architecture of multilayer feedforward network..........................................5
    2.3 Practical application of back-propagation network......................................8
    3 Simulated Study for Bilinear Time Series 12
    4 On Forecasting Problem for Exchange Rates 17
    4.1 General discussion......................................................................................17
    4.2 Forecasting Performance.............................................................................18
    5 Conclusions 27
    A Tendencies of simulated bilinear time series 31    		zh_TW
    dc.source.uri (Source URI) http://thesis.lib.nccu.edu.tw/record/#B2002004238en_US
    dc.subject (Keywords) 神經網路zh_TW
    dc.subject (Keywords) 雙線型模式zh_TW
    dc.subject (Keywords) 倒傳遞網路zh_TW
    dc.subject (Keywords) 匯率zh_TW
    dc.subject (Keywords) neural networksen_US
    dc.subject (Keywords) bilinear modelen_US
    dc.subject (Keywords) backpropagationen_US
    dc.subject (Keywords) exchange ratesen_US
    dc.title (Title) 非線型時間序列之穩健預測zh_TW
    dc.title (Title) Robust Forecasting For Nonlinear Time Seriesen_US
    dc.type (Data Type) thesisen_US
    dc.relation.reference (Reference) [1] Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis: Fore-casting and Control. 2nd ed. San Francisco : Holden-Day.
    [2] Brockett,R.W.(1976).Volterra series and geometric control theory. Au-tomatica, 12. 167-176.
    [3] Chan, W.S. and Tong, H. (1986). On test for non-linearity in time series analysis. J. Forecasting, 5, 217-28.
    [4] Cynbento, G., (1989). Approximation by superposition of a sigmoidal function, Mathematics of Control, Signals and Systems, 2, 303-314.
    [5] De Gooijer, J.G. and Kumar, K.(1992). Some recent developments in nonlinear time series modelling, testing and forecasting. International Journal of Forecasting, 8, 135-156.
    [6] Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of U.K. inflation. Econometrica, 50, 987-1008.
    [7] Funahashi, K. I., (1989). On the approximate of continuous mappings by neural networks, Neural Networks, 2, 183-192.
    [8] Granger, C.W.J. and Anderson, A. P. (1978). An Introduction to Bi-linear Time Series Models. Vandenhoeck and Ruprech, Gottingen.
    [9] Granger, C.W.J. (1991). Developments in the nonlinear analysis of economic series. Scand. J. Of Economics. 93(2), 263-276.
    [10] Grosberg, S. (1988). Studics of Mind and Brain: Neural Principles of Learning, Perception, Development, Cognition and Motor Control. Boston, MA: Reidel.
    [11] Guegan, D. and Pham, T.D. (1992). Power of the score test against bilinear time series models. Statistica Sinica, Vol. 2, 1, 157-169.
    [12] Hecht-Nielsen, R., (1989). Neurocomputing, IEEE Spectrum, March, 36-41.
    [13] Hinich, M. (1982). Testing for Gaussianity and linearity of a stationary time series. J. Time series Analysis, Vol.3, No.3, 169-76.
    [14] Kolen, J. F. and Goel, A. K. (1991). Learning in parallel distributed processing networks: computational complexity and information con-tent. IEEE Transactions on Systems, Man, and Cybernetics, 21, 2, 359-367.
    [15] Kosko, B. (1992). Neural Networks for Signal Processing, Prentice Hall, Englewood Cliffs, NJ.
    [16] Lapedes, A., and Farber, R., (1988). How Neural Nets Work. The-oretical Division. Los Alamos National Laboratory Los Alamos, NM 87545.
    [17] Luukkonen, R., Saikkonen P. and Terasvirta, T. (1988). Testing lin-earity against smooth transition autocorrelation models. Biometrica, 75, 491-500.
    [18] McKenzie, E. (1985). Some simple models for discrete variate time series. In Time Series Analysis in Water Resources. (ed. K. W. Hipel), 645-650, AM. Water Res. Assoc.
    [19] Priestley, M. B. (1980). State-dependent models: a general approach to nonlinear time series. J. Time Series Anal. 1, 47-71.
    [20] Saikkonen, P. and Luukkonen, K. (1988). Lagrange multiplier test for testing non-linearities in time series models. Scand. J. of Statistics, 15, 55-68.
    [21] Saikkonen, P. and Luukkonen, K. (1991). Power properties of a time series linearity test against some simple bilinear alternatives. Statistica Sinica, Vol. 1, 2, 453-464.
    [22] Subba Rao, T. and Gabr, M. M. (1984). An Introduction to Bispectral Analysis and Bilinear Time Series Models. Lecture Notes in statistics, Springer- Verlag, London.
    [23] Tjoostheim, D.(1986). Some doubly stochastic time series models J. Time Ser. Analysis, 7, 51-72.
    [24] Tong, H. And Lim, K. S. (1980). Threshold autoregression, limit cycles and cyclical data. J. Roy. Statist. Soc. Ser. B, 42, 245-292.
    [25] Tsay, R. S. (1989). Testing and modeling threshold autoregressive pro-cesses. Journal of the American Statistical Association, 84, 231-240.
    [26] Tsay, R. S. (1991). Detecting and modeling nonlinearity in univariate time series analysis. Statistica Sinica, Vol. 1, 2, 431-51.
    [27] Weiss, A. A. (1986). ARCH and bilinear time series models: compari-son and combination. J. Business Economic Statistics. Vol. 4, No. 1, 59-70.
    [28] Wu, B., Liou, W. And Chen, Y. (1992). Robust forecasting for the stochastic models and chaotic models. J. Chinese Statist. Assoc. Vol.30, No. 2, 169-189.
    [29] Wu, B. And Shih, N. (1992). On the identification problem for bilinear time series models. J. Statist. Comput. Simul. Vol.43. 129-161.    		zh_TW

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