Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/88741| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 吳柏林 | zh_TW |
| dc.contributor.advisor | Wu, Berlin | en_US |
| dc.contributor.author | 劉勇杉 | zh_TW |
| dc.contributor.author | Liu, Yung Shan | en_US |
| dc.creator | 劉勇杉 | zh_TW |
| dc.creator | Liu, Yung Shan | en_US |
| dc.date | 1993 | en_US |
| dc.date.accessioned | 2016-04-29T08:32:31Z | - |
| dc.date.available | 2016-04-29T08:32:31Z | - |
| dc.date.issued | 2016-04-29T08:32:31Z | - |
| dc.identifier | B2002004238 | en_US |
| dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/88741 | - |
| dc.description | 碩士 | zh_TW |
| dc.description | 國立政治大學 | zh_TW |
| dc.description | 應用數學系 | zh_TW |
| dc.description | 80155004 | zh_TW |
| dc.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 | zh_TW |
| dc.description.abstract | With rapid development at the study of time series, the | en_US |
| dc.description.tableofcontents | 1 Introduction 1\r\n2 Neural Networks and Model-free Forecast 4\r\n2.1 Motivation for forecasting nonlinear time series..........................................4\r\n2.2 Architecture of multilayer feedforward network..........................................5\r\n2.3 Practical application of back-propagation network......................................8\r\n3 Simulated Study for Bilinear Time Series 12\r\n4 On Forecasting Problem for Exchange Rates 17\r\n 4.1 General discussion......................................................................................17\r\n 4.2 Forecasting Performance.............................................................................18\r\n5 Conclusions 27\r\nA Tendencies of simulated bilinear time series 31 | zh_TW |
| dc.source.uri | http://thesis.lib.nccu.edu.tw/record/#B2002004238 | en_US |
| dc.subject | 神經網路 | zh_TW |
| dc.subject | 雙線型模式 | zh_TW |
| dc.subject | 倒傳遞網路 | zh_TW |
| dc.subject | 匯率 | zh_TW |
| dc.subject | neural networks | en_US |
| dc.subject | bilinear model | en_US |
| dc.subject | backpropagation | en_US |
| dc.subject | exchange rates | en_US |
| dc.title | 非線型時間序列之穩健預測 | zh_TW |
| dc.title | Robust Forecasting For Nonlinear Time Series | en_US |
| dc.type | thesis | en_US |
| dc.relation.reference | [1] Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis: Fore-casting and Control. 2nd ed. San Francisco : Holden-Day.\r\n[2] Brockett,R.W.(1976).Volterra series and geometric control theory. Au-tomatica, 12. 167-176.\r\n[3] Chan, W.S. and Tong, H. (1986). On test for non-linearity in time series analysis. J. Forecasting, 5, 217-28.\r\n[4] Cynbento, G., (1989). Approximation by superposition of a sigmoidal function, Mathematics of Control, Signals and Systems, 2, 303-314.\r\n[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.\r\n[6] Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of U.K. inflation. Econometrica, 50, 987-1008.\r\n[7] Funahashi, K. I., (1989). On the approximate of continuous mappings by neural networks, Neural Networks, 2, 183-192.\r\n[8] Granger, C.W.J. and Anderson, A. P. (1978). An Introduction to Bi-linear Time Series Models. Vandenhoeck and Ruprech, Gottingen.\r\n[9] Granger, C.W.J. (1991). Developments in the nonlinear analysis of economic series. Scand. J. Of Economics. 93(2), 263-276.\r\n[10] Grosberg, S. (1988). Studics of Mind and Brain: Neural Principles of Learning, Perception, Development, Cognition and Motor Control. Boston, MA: Reidel.\r\n[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.\r\n[12] Hecht-Nielsen, R., (1989). Neurocomputing, IEEE Spectrum, March, 36-41.\r\n[13] Hinich, M. (1982). Testing for Gaussianity and linearity of a stationary time series. J. Time series Analysis, Vol.3, No.3, 169-76.\r\n[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.\r\n[15] Kosko, B. (1992). Neural Networks for Signal Processing, Prentice Hall, Englewood Cliffs, NJ.\r\n[16] Lapedes, A., and Farber, R., (1988). How Neural Nets Work. The-oretical Division. Los Alamos National Laboratory Los Alamos, NM 87545.\r\n[17] Luukkonen, R., Saikkonen P. and Terasvirta, T. (1988). Testing lin-earity against smooth transition autocorrelation models. Biometrica, 75, 491-500.\r\n[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.\r\n[19] Priestley, M. B. (1980). State-dependent models: a general approach to nonlinear time series. J. Time Series Anal. 1, 47-71.\r\n[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.\r\n[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.\r\n[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.\r\n[23] Tjoostheim, D.(1986). Some doubly stochastic time series models J. Time Ser. Analysis, 7, 51-72.\r\n[24] Tong, H. And Lim, K. S. (1980). Threshold autoregression, limit cycles and cyclical data. J. Roy. Statist. Soc. Ser. B, 42, 245-292.\r\n[25] Tsay, R. S. (1989). Testing and modeling threshold autoregressive pro-cesses. Journal of the American Statistical Association, 84, 231-240.\r\n[26] Tsay, R. S. (1991). Detecting and modeling nonlinearity in univariate time series analysis. Statistica Sinica, Vol. 1, 2, 431-51.\r\n[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.\r\n[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.\r\n[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 |
| item.fulltext | With Fulltext | - |
| item.openairetype | thesis | - |
| item.cerifentitytype | Publications | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_46ec | - |
| item.grantfulltext | open | - |
| Appears in Collections: | 學位論文 | |
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