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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 29-四月-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, Berlin en_US dc.contributor.author (作者) 劉勇杉 zh_TW dc.contributor.author (作者) Liu, Yung Shan en_US dc.creator (作者) 劉勇杉 zh_TW dc.creator (作者) Liu, Yung Shan en_US dc.date (日期) 1993 en_US dc.date.accessioned 29-四月-2016 16:32:31 (UTC+8) - dc.date.available 29-四月-2016 16:32:31 (UTC+8) - dc.date.issued (上傳時間) 29-四月-2016 16:32:31 (UTC+8) - dc.identifier (其他 識別碼) B2002004238 en_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) 80155004 zh_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, the en_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/#B2002004238 en_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 networks en_US dc.subject (關鍵詞) bilinear model en_US dc.subject (關鍵詞) backpropagation en_US dc.subject (關鍵詞) exchange rates en_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 Series en_US dc.type (資料類型) thesis en_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
