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題名 相對移動率應用在區間時間序列預測及其效率評估
The Application of Relative Moving Ratio for Forecasting and performance Evaluation in Interval Time Series
作者 李治陞
Li, Chih-Sheng
貢獻者 劉明郎<br>吳柏林
Liu, Ming-Long<br>Wu, Berlin
李治陞
Li, Chih-Sheng
關鍵詞 模糊時間序列
反模糊化
區間預測
相對移動率
門檻自廻規模型
fuzzy time series
defuzzification
interval prediction
relative moving ratio
threshold autoregressive models
日期 2011
上傳時間 30-Oct-2012 11:27:57 (UTC+8)
摘要 時間序列是用來預測未來趨勢的一種重要技術，然而在實務上建構時間序列模型時，參數很難有效估計。原因可能來自於時間序列本身的模糊性質，而導致參數的不確定性使得預測結果產生極大誤差。如果將參數模糊化引進時間序列的模型中，往往過於複雜。本論文提出相對移動率為新的模糊時間序列建構方法，讓原本具有模糊性質的時間序列經由反模糊化(defuzzification)後，以點估計的方式估計起始中心點，經由適當的修正調整為較佳的中心點以及半徑，建立有效的區間時間序列。並將相對移動率引進門檻自廻規模型中，取代原有之門檻值設定，並建立區間時間序列。最後，我們使用台灣加權股價指數為例，以本論文所提出之方法進行區間預測及效率評估。
The time series is an important technology that is used to predict future trends, however in the real world, parameter is difficult to estimate effectively when we construct a time series model due to the of the fuzzy property of the times series data. The estimated parameters in the time series will cause a big error due to the uncertainty of fuzzy data. It is too complex to introduce the fuzzy parameters into the time series model. In this thesis, we propose relative moving ratio as a new criteria in constructing procedure of an interval time series. We defuzzify a fuzzy data and use point estimation to obtain an initial center, then we adjust the center and radius making it more appropriately. The resulting center and radius is then become an interval time series that can be use to forecast an interval data. We also apply relative moving ratio in threshold autoregressive models by replacing the threshold in constructing interval time series. Finally, in empirical studies chapter, we use Taiwan weighted Stock Index as examples to evaluate the performance of the proposed two methods in building the interval time series.
參考文獻 吳柏林(1995)，時間數列分析導論，華泰書局，台北。
吳柏林(2005)，模糊統計導論方法與應用，五南出版社，台北。
吳柏林、阮亨中(2000)，模糊數學與統計應用，俊傑書局，台北。
吳柏林、林玉鈞(2002)，模糊時間數列分析與預測—以台灣地區加權股價指數為例，應用數學學報，第25卷，第一期，頁67-76。
楊奕農(2009)，時間序列分析：經濟與財務上之應用，雙葉書廊，台北。
Akaike, H. (1973). Information theory and an extension of maximum likelihood principle, Second International Symposium on Information Theory 1, 267-281.
Box, G. P. and Jenkins, G. M. (1976). Time series analysis forecasting and control. San Francisco: Holden-Day.
Byers, J. D. and Peel, D. A. (1995). Evidence on volatility spillovers in the interwar floating exchange rate period based on high/low prices, Applied Economics Letters 2(10), 394-396.
Chow, G. C. (1960), Tests of equality between sets of coefficients in two linear regressions, Econometrica 28(3), 591-605.
Donald W. K. A. and Werner P. (1994). Optimal tests when a nuisance parameter is present only under the alternative, Econometrica 62(6), 1383-1414.
Graham, B. P. and Newell, R. B. (1989). Fuzzy adaptive control of a first-order process. Fuzzy sets and system 31, 47-65.
Haggan V. and Ozaki T. (1980). Amplitude-dependent exponential AR model fitting for non-linear random vibrations, in Time Series, O. D. Anderson ed., North-Holland, Amsterdam.
Hsu, H. L. (2011). Interval Time Series Analysis with Forecasting Efficiency Evaluation, Doctorial Thesis, Department of Mathematical Science, National Chengchi University, Taipei, Taiwan.
Kumar, K. and Wu, B. (2001). Detection of change points in time series analysis with fuzzy statistics, International Journal of Systems Science 32(9), 1185-1192.
Subba R. T. and Gabr M. (1980). A test for linearity of stationary time series analysis, Journal of Time Series Analysis 1(1), 145-158.
Tong, R. M. (1978). Synthesis of fuzzy models for industrial processes. Int. J. Gen. 4, 143-162.
Tong H. and Lim K. S. (1980). Threshold Autoregressive, Limit Cycles and Cyclical Data (with Discussion), Journal of the Royal Statistical Society. Series B 42(3), 245-292.
Wu, B. (2011). Efficiency Evaluation in Time Management for School Administration with Fuzzy Data, Technical Report, Department of Mathematical Science, National Chengchi University, Taipei, Taiwan.
Zadeh, L. A. (1965). Fuzzy sets, Information and Control 8, 338-353.
Zhou H. D. (2005). Nonlinearity or structural break - data mining in evolving financial data sets from a Bayesian model combination perspective, Proceedings of the 38th Hawaii International Conference on System Sciences, Hawaii, U.S.A.
描述 碩士
國立政治大學
應用數學研究所
98751007
100
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0098751007
資料類型 thesis
dc.contributor.advisor 劉明郎<br>吳柏林zh_TW
dc.contributor.advisor Liu, Ming-Long<br>Wu, Berlinen_US
dc.contributor.author (Authors) 李治陞zh_TW
dc.contributor.author (Authors) Li, Chih-Shengen_US
dc.creator (作者) 李治陞zh_TW
dc.creator (作者) Li, Chih-Shengen_US
dc.date (日期) 2011en_US
dc.date.accessioned 30-Oct-2012 11:27:57 (UTC+8)-
dc.date.available 30-Oct-2012 11:27:57 (UTC+8)-
dc.date.issued (上傳時間) 30-Oct-2012 11:27:57 (UTC+8)-
dc.identifier (Other Identifiers) G0098751007en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54645-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 應用數學研究所zh_TW
dc.description (描述) 98751007zh_TW
dc.description (描述) 100zh_TW
dc.description.abstract (摘要) 時間序列是用來預測未來趨勢的一種重要技術，然而在實務上建構時間序列模型時，參數很難有效估計。原因可能來自於時間序列本身的模糊性質，而導致參數的不確定性使得預測結果產生極大誤差。如果將參數模糊化引進時間序列的模型中，往往過於複雜。本論文提出相對移動率為新的模糊時間序列建構方法，讓原本具有模糊性質的時間序列經由反模糊化(defuzzification)後，以點估計的方式估計起始中心點，經由適當的修正調整為較佳的中心點以及半徑，建立有效的區間時間序列。並將相對移動率引進門檻自廻規模型中，取代原有之門檻值設定，並建立區間時間序列。最後，我們使用台灣加權股價指數為例，以本論文所提出之方法進行區間預測及效率評估。zh_TW
dc.description.abstract (摘要) The time series is an important technology that is used to predict future trends, however in the real world, parameter is difficult to estimate effectively when we construct a time series model due to the of the fuzzy property of the times series data. The estimated parameters in the time series will cause a big error due to the uncertainty of fuzzy data. It is too complex to introduce the fuzzy parameters into the time series model. In this thesis, we propose relative moving ratio as a new criteria in constructing procedure of an interval time series. We defuzzify a fuzzy data and use point estimation to obtain an initial center, then we adjust the center and radius making it more appropriately. The resulting center and radius is then become an interval time series that can be use to forecast an interval data. We also apply relative moving ratio in threshold autoregressive models by replacing the threshold in constructing interval time series. Finally, in empirical studies chapter, we use Taiwan weighted Stock Index as examples to evaluate the performance of the proposed two methods in building the interval time series.en_US
dc.description.tableofcontents 目錄
謝辭 iv
摘要 v
Abstract vi
目錄 vii

第一章　緒論 1

第二章　模糊時間序列分析 3
2.1　序言 3
2.2　模糊時間序列模型 7
2.3　模糊時間序列模型之預測方法 9

第三章　模糊時間序列模型之效率評估 14
3.1　模糊區間之定位及測量辦法 14
3.2　模糊時間序列模型之預測指標 16

第四章　實證分析 18

第五章　結論 35

參考文獻 36		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0098751007en_US
dc.subject (關鍵詞) 模糊時間序列zh_TW
dc.subject (關鍵詞) 反模糊化zh_TW
dc.subject (關鍵詞) 區間預測zh_TW
dc.subject (關鍵詞) 相對移動率zh_TW
dc.subject (關鍵詞) 門檻自廻規模型zh_TW
dc.subject (關鍵詞) fuzzy time seriesen_US
dc.subject (關鍵詞) defuzzificationen_US
dc.subject (關鍵詞) interval predictionen_US
dc.subject (關鍵詞) relative moving ratioen_US
dc.subject (關鍵詞) threshold autoregressive modelsen_US
dc.title (題名) 相對移動率應用在區間時間序列預測及其效率評估zh_TW
dc.title (題名) The Application of Relative Moving Ratio for Forecasting and performance Evaluation in Interval Time Seriesen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 吳柏林(1995)，時間數列分析導論，華泰書局，台北。
吳柏林(2005)，模糊統計導論方法與應用，五南出版社，台北。
吳柏林、阮亨中(2000)，模糊數學與統計應用，俊傑書局，台北。
吳柏林、林玉鈞(2002)，模糊時間數列分析與預測—以台灣地區加權股價指數為例，應用數學學報，第25卷，第一期，頁67-76。
楊奕農(2009)，時間序列分析：經濟與財務上之應用，雙葉書廊，台北。
Akaike, H. (1973). Information theory and an extension of maximum likelihood principle, Second International Symposium on Information Theory 1, 267-281.
Box, G. P. and Jenkins, G. M. (1976). Time series analysis forecasting and control. San Francisco: Holden-Day.
Byers, J. D. and Peel, D. A. (1995). Evidence on volatility spillovers in the interwar floating exchange rate period based on high/low prices, Applied Economics Letters 2(10), 394-396.
Chow, G. C. (1960), Tests of equality between sets of coefficients in two linear regressions, Econometrica 28(3), 591-605.
Donald W. K. A. and Werner P. (1994). Optimal tests when a nuisance parameter is present only under the alternative, Econometrica 62(6), 1383-1414.
Graham, B. P. and Newell, R. B. (1989). Fuzzy adaptive control of a first-order process. Fuzzy sets and system 31, 47-65.
Haggan V. and Ozaki T. (1980). Amplitude-dependent exponential AR model fitting for non-linear random vibrations, in Time Series, O. D. Anderson ed., North-Holland, Amsterdam.
Hsu, H. L. (2011). Interval Time Series Analysis with Forecasting Efficiency Evaluation, Doctorial Thesis, Department of Mathematical Science, National Chengchi University, Taipei, Taiwan.
Kumar, K. and Wu, B. (2001). Detection of change points in time series analysis with fuzzy statistics, International Journal of Systems Science 32(9), 1185-1192.
Subba R. T. and Gabr M. (1980). A test for linearity of stationary time series analysis, Journal of Time Series Analysis 1(1), 145-158.
Tong, R. M. (1978). Synthesis of fuzzy models for industrial processes. Int. J. Gen. 4, 143-162.
Tong H. and Lim K. S. (1980). Threshold Autoregressive, Limit Cycles and Cyclical Data (with Discussion), Journal of the Royal Statistical Society. Series B 42(3), 245-292.
Wu, B. (2011). Efficiency Evaluation in Time Management for School Administration with Fuzzy Data, Technical Report, Department of Mathematical Science, National Chengchi University, Taipei, Taiwan.
Zadeh, L. A. (1965). Fuzzy sets, Information and Control 8, 338-353.
Zhou H. D. (2005). Nonlinearity or structural break - data mining in evolving financial data sets from a Bayesian model combination perspective, Proceedings of the 38th Hawaii International Conference on System Sciences, Hawaii, U.S.A.		zh_TW