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題名 非線型時間序列之穩健預測
Robust Forecasting For Nonlinear Time Series
作者 劉勇杉
Liu, Yung Shan
貢獻者 吳柏林
Wu, Berlin
劉勇杉
Liu, Yung Shan
關鍵詞 神經網路
雙線型模式
倒傳遞網路
匯率
neural networks
bilinear model
backpropagation
exchange rates
日期 1993
上傳時間 29-Apr-2016 16:32:31 (UTC+8)
摘要 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
參考文獻 [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.
描述 碩士
國立政治大學
應用數學系
80155004
資料來源 http://thesis.lib.nccu.edu.tw/record/#B2002004238
資料類型 thesis
dc.contributor.advisor 吳柏林zh_TW
dc.contributor.advisor Wu, Berlinen_US
dc.contributor.author (Authors) 劉勇杉zh_TW
dc.contributor.author (Authors) Liu, Yung Shanen_US
dc.creator (作者) 劉勇杉zh_TW
dc.creator (作者) Liu, Yung Shanen_US
dc.date (日期) 1993en_US
dc.date.accessioned 29-Apr-2016 16:32:31 (UTC+8)-
dc.date.available 29-Apr-2016 16:32:31 (UTC+8)-
dc.date.issued (上傳時間) 29-Apr-2016 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 (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 應用數學系zh_TW
dc.description (描述) 80155004zh_TW
dc.description.abstract (摘要) 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 (摘要) 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 (資料來源) http://thesis.lib.nccu.edu.tw/record/#B2002004238en_US
dc.subject (關鍵詞) 神經網路zh_TW
dc.subject (關鍵詞) 雙線型模式zh_TW
dc.subject (關鍵詞) 倒傳遞網路zh_TW
dc.subject (關鍵詞) 匯率zh_TW
dc.subject (關鍵詞) neural networksen_US
dc.subject (關鍵詞) bilinear modelen_US
dc.subject (關鍵詞) backpropagationen_US
dc.subject (關鍵詞) exchange ratesen_US
dc.title (題名) 非線型時間序列之穩健預測zh_TW
dc.title (題名) Robust Forecasting For Nonlinear Time Seriesen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [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