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題名 季節性時間序列之預測─類神經網路模式之探討
Forecasting Seasonal Time Series : A Neural Network Approach
作者 賴家瑞
Lia, Chia Jui
貢獻者 吳柏林
Wu, Berlin
賴家瑞
Lia, Chia Jui
關鍵詞 季節性時間序列
神經網路
移動學習法
離群值
預測
seasonal time series
neural networks
shifting learning method
outliers
forecasting
日期 1993
上傳時間 29-Apr-2016 16:32:33 (UTC+8)
摘要 本論文主要研究以類神經網路模式預測季節性時間序列之有效性。利用適
We investigate the effectiveness of neural networks for
參考文獻 Barron, A. R> (1991) Approximation bounds for superpositions of a sigmoid function. Proceedings of IEEE International Symposium on Information Theory. IEEE press.
     Box, G. E. P. and Jenkins, G. G. (1976) Time Series Analysis : Forecasting and Control. 2nd-ed. San Francisco : Holden-Day.
     Chen, C. And Liu, L. M. (1993) Forecasting time series with outliers. Journal of Forecasting, Vol. 12, 13-35.
     Conradsen, K. and Spliid, H. (1981) A seasonal adjustment filter for use in Box Jenkins analyses of seasonal time sereis. Applied Statistics, Vol. 30, No.1, 172-177.
     Cybenko, G. (1989) Approximation by superposition of a sigmoidal function. Methematics of control, signals and systems, Vol 2, 303-314.
     Dagum, E. B. (1975) Seasonal factor forecasts from ARIMA models, paper presented at the 40th session of the International Statistic Institute, Warsaw, Poland.
     Funahashi, K. I. (1989) On the approximate of continuous mappings by neural networks. Neural Networks, Vol. 2, 183-192.
     Hecht-Nielsen, R. (1990) Neurocomputing. Addisom-Wesley.
     Hornik, K., Stinchcombe, M. and White, H. (1989) Multilayer feedfoward networks are universal approximators, Neural Networks, Vol. 2, 359-366.
     Lee, K. Y. , Cha, Y. T. and Part, J. H. (1991) Short-term load forecasting using an artificial neural network. IEEE Winter Power Meeting , paper No.91WM 199-0 PWRS.
     Murthy, D. N. P. (1985) Parameter estimation with periodic observations. Journal of Time Sereis, Vol. 6, No.2. 91-96.
     Osborn, D. (1991) The implications of periodically varying coefficients for seasonal time-series process. Journal of Econometrics, Vol. 48, 373-384.
     
     
     Park, D. C. , El-Sharkawi, M. A. , Marks, R. J. , Atlas, L. E. and Damborg, M.J. (1990) Electric load forecasting using an artifical neural network. IEEE Summer Power Meeting, paper No.90SM 377-2 PWRS.
     Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986) Learning representations by back-propagation errors. Nature, Vol. 323, 533-536.
     Vogl, T. P., Mangis, J. K., Rigler, A. K., Zink, W. T. and Alkon, D. L. (1988) Accelerating the convergence of the back-progation method. Biological Cybernetics, Vol.59, 257-263.
     Weiglend, A., Huberman, B. And Rumelhar, D. (1990) Predicting the future : a connectionist approach. International Journal of Neural Systems, Vol. 1, No.3, 193-209.
     Werbos, P. (1988) Generalization of back-propagation with application to recurrent gas market model. Neural Networks, Vol. 1. 339-256.
     White, H. (1981) Consequences and detection of misspecified nonlinaer regulartion models. Journal of the American Statistic Association, Vol. 76, 419-433.
     Wu, B. And Yu, C. (1992) Forecasting for nonstationary time series : a neural networks approach. Proceedings of the mid-America Chinese professional annual convention.
描述 碩士
國立政治大學
應用數學系
80155005
資料來源 http://thesis.lib.nccu.edu.tw/record/#B2002004239
資料類型 thesis
dc.contributor.advisor 吳柏林zh_TW
dc.contributor.advisor Wu, Berlinen_US
dc.contributor.author (Authors) 賴家瑞zh_TW
dc.contributor.author (Authors) Lia, Chia Juien_US
dc.creator (作者) 賴家瑞zh_TW
dc.creator (作者) Lia, Chia Juien_US
dc.date (日期) 1993en_US
dc.date.accessioned 29-Apr-2016 16:32:33 (UTC+8)-
dc.date.available 29-Apr-2016 16:32:33 (UTC+8)-
dc.date.issued (上傳時間) 29-Apr-2016 16:32:33 (UTC+8)-
dc.identifier (Other Identifiers) B2002004239en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/88742-
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) 80155005zh_TW
dc.description.abstract (摘要) 本論文主要研究以類神經網路模式預測季節性時間序列之有效性。利用適zh_TW
dc.description.abstract (摘要) We investigate the effectiveness of neural networks foren_US
dc.description.tableofcontents 1. Introduction..............................................................................................................1
     2. The Use of Neural Networks as a Predictor.............................................................3
     2.1 Neural networks system and neurocomputing...................................................3
     2.2 Neural networks and model-free predictor.........................................................7
     2.3 Robustness offorecasting....................................................................................9
     3. Seasonal Time Series Back-Propagation Networks................................................11
     3.1 Characteristics of seasonal time series..............................................................11
     3.2 Constructing a seasonal time series networks...................................................15
     4. An Empirical Study.................................................................................................19
     5. Conclusion...............................................................................................................28
     Reference.......................................................................................................................29		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#B2002004239en_US
dc.subject (關鍵詞) 季節性時間序列zh_TW
dc.subject (關鍵詞) 神經網路zh_TW
dc.subject (關鍵詞) 移動學習法zh_TW
dc.subject (關鍵詞) 離群值zh_TW
dc.subject (關鍵詞) 預測zh_TW
dc.subject (關鍵詞) seasonal time seriesen_US
dc.subject (關鍵詞) neural networksen_US
dc.subject (關鍵詞) shifting learning methoden_US
dc.subject (關鍵詞) outliersen_US
dc.subject (關鍵詞) forecastingen_US
dc.title (題名) 季節性時間序列之預測─類神經網路模式之探討zh_TW
dc.title (題名) Forecasting Seasonal Time Series : A Neural Network Approachen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Barron, A. R> (1991) Approximation bounds for superpositions of a sigmoid function. Proceedings of IEEE International Symposium on Information Theory. IEEE press.
     Box, G. E. P. and Jenkins, G. G. (1976) Time Series Analysis : Forecasting and Control. 2nd-ed. San Francisco : Holden-Day.
     Chen, C. And Liu, L. M. (1993) Forecasting time series with outliers. Journal of Forecasting, Vol. 12, 13-35.
     Conradsen, K. and Spliid, H. (1981) A seasonal adjustment filter for use in Box Jenkins analyses of seasonal time sereis. Applied Statistics, Vol. 30, No.1, 172-177.
     Cybenko, G. (1989) Approximation by superposition of a sigmoidal function. Methematics of control, signals and systems, Vol 2, 303-314.
     Dagum, E. B. (1975) Seasonal factor forecasts from ARIMA models, paper presented at the 40th session of the International Statistic Institute, Warsaw, Poland.
     Funahashi, K. I. (1989) On the approximate of continuous mappings by neural networks. Neural Networks, Vol. 2, 183-192.
     Hecht-Nielsen, R. (1990) Neurocomputing. Addisom-Wesley.
     Hornik, K., Stinchcombe, M. and White, H. (1989) Multilayer feedfoward networks are universal approximators, Neural Networks, Vol. 2, 359-366.
     Lee, K. Y. , Cha, Y. T. and Part, J. H. (1991) Short-term load forecasting using an artificial neural network. IEEE Winter Power Meeting , paper No.91WM 199-0 PWRS.
     Murthy, D. N. P. (1985) Parameter estimation with periodic observations. Journal of Time Sereis, Vol. 6, No.2. 91-96.
     Osborn, D. (1991) The implications of periodically varying coefficients for seasonal time-series process. Journal of Econometrics, Vol. 48, 373-384.
     
     
     Park, D. C. , El-Sharkawi, M. A. , Marks, R. J. , Atlas, L. E. and Damborg, M.J. (1990) Electric load forecasting using an artifical neural network. IEEE Summer Power Meeting, paper No.90SM 377-2 PWRS.
     Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986) Learning representations by back-propagation errors. Nature, Vol. 323, 533-536.
     Vogl, T. P., Mangis, J. K., Rigler, A. K., Zink, W. T. and Alkon, D. L. (1988) Accelerating the convergence of the back-progation method. Biological Cybernetics, Vol.59, 257-263.
     Weiglend, A., Huberman, B. And Rumelhar, D. (1990) Predicting the future : a connectionist approach. International Journal of Neural Systems, Vol. 1, No.3, 193-209.
     Werbos, P. (1988) Generalization of back-propagation with application to recurrent gas market model. Neural Networks, Vol. 1. 339-256.
     White, H. (1981) Consequences and detection of misspecified nonlinaer regulartion models. Journal of the American Statistic Association, Vol. 76, 419-433.
     Wu, B. And Yu, C. (1992) Forecasting for nonstationary time series : a neural networks approach. Proceedings of the mid-America Chinese professional annual convention.		zh_TW