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題名 應用基因演算法決定SETAR門檻區間及其應用
Use genetic algorithms to determine the SETAR threshold interval and Its Applications
作者 江增堂
貢獻者 吳柏林
Wu, Berlin
江增堂
關鍵詞 非線性
區間軟計算
模糊分析
基因演算法
門檻自迴歸
門檻區間
nonlinear
soft computing
fuzzy analysis
genetic algorithms
SETAR
threshold interval
日期 2012
上傳時間 1-Mar-2013 09:25:46 (UTC+8)
摘要 近年來，面對傳統線性時間序列的預測問題，有許多技術上的改良而被大量廣泛的使用，但是線性模式往往無法處理常常發生結構改變(structural changes)的問題，這使得非線性(nonlinearity)時間序列轉折點的研究越來越受到重視，利用非線性時間序列解決實例更可以貼近真實情況。再者，隨著模糊理論的蓬勃發展以及區間軟計算(soft computing)的成熟，相較於點估計預測方法所需的嚴格假設，區間估計方法的假設寬鬆許多並且能符合實際情況，可以提供給決策者更彈性的選擇。本文將應用基因演算法(genetic algorithms)針對模糊區間資料(fuzzy data)作模糊分析(fuzzy analysis)，找出資料轉折的門檻區間(threshold interval)，藉此發展出非線性的區間門檻自迴歸模式(interval SETAR model)，最後以台股為例，建構出門檻自迴歸模型與傳統區間ARIMA模式比較，藉此探討其預測方法的效率評估與準確性。
In recent years, in the face of traditional linear time series forecasting problems, there are many technical improvements and widely used. But linear model are often unable to deal with the problem often happens structural changes, which makes the nonlinear turning point for the study of the time series more and more attention. Use nonlinear time series more close to the real situation. Moreover, with the fuzzy theories flourish and soft computing mature, compared to the point estimate methods required strict assumptions, interval estimation method which without many assumptions can meet the actual situation. It can be provided to decision-makers more flexibility of choice. In this paper, the application of genetic algorithms for fuzzy data to identify structural changes interval (threshold interval), so as to develop the nonlinear range threshold autoregressive mode (interval SETAR model), and finally, for example, the Taiwan stock market, construct a threshold autoregression model with the traditional interval ARIMA model to investigate the prediction method efficiency and accuracy.
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1期，頁67-76。
程友梅(1995)，轉移型時間序列的認定。國立政治大學統計系碩士論文。
張新發(1996)，遺傳演算法在門檻自迴歸模式(d,r)值估計的應用。國立政治大學統計系碩士論文。
楊亦農(2009)，時間序列分析：經濟與財務上之應用，雙葉書廊，台北。
F.-M. Tseng and G.-H. Tzeng (2002) a fuzzy seasonal ARIMA model for forecasting. Fuzzy sets and systems, 126(3), 367-376.
H. T. Nguyen and B. Wu (2006) Fundamentals of Statistics with Fuzzy Data. New York：Springer.
Hansen, B.E. (1997). Inference in TAR Models, Studies in Nonlinear Dynamics and Econometrics, 2, 1-14.
Hsu, H.L. (2008). Evaluating forecasting performance for interval data. Computers and Mathematics with Applications 56, 2155-2163.
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.
Ludermir, T. B. (2008). Forecasting models for interval-valued time series. Neurocomputing 71, 3228-3238.
M. Bleaney, N. Gemmell, R.Kneller(1989) Testing the endogenous growth model: public expenditure, taxation, and growth over the long run.
M. Khashei, S.R. Hejazi and M. Bijari (2008) A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy sets and systems, 159, 769-786.
S.K. Chang (2007) On the Testing Hypotheses of Mean and Variance for Interval Data. Management Science & Statistical Decision, 4(2), 63-69.
Tong, H. & Lim, K. S. (1980). Journal of the Royal Statistical Society, Series B,"Threshold Autoregression, Limit Cycles and Cyclical Data (with discussion)", 42, 245-292.
Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press.
Tseng, F.M., Tseng, G.H., Yu, H.C., and Yuan, B.C. (2001). Fuzzy ARIMA model for forecasting the foreign exchange market. Fuzzy sets and systems 118(1), 9-19.
V. Kreinovich , H. T. Nguyen and B. Wu (2007) On-line algorithms for computing mean and variance of interval data, and their use in intelligent systems. Information Sciences, 177, 3228-3238.
Wu, B and Hung, S. (1999). Fuzzy Sets and Systems. A fuzzy identification procedure for nonlinear time series with example on ARCH and bilinear models. 108, 275-287.
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.
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
99751010
101
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0099751010
資料類型 thesis
dc.contributor.advisor 吳柏林zh_TW
dc.contributor.advisor Wu, Berlinen_US
dc.contributor.author (Authors) 江增堂zh_TW
dc.creator (作者) 江增堂zh_TW
dc.date (日期) 2012en_US
dc.date.accessioned 1-Mar-2013 09:25:46 (UTC+8)-
dc.date.available 1-Mar-2013 09:25:46 (UTC+8)-
dc.date.issued (上傳時間) 1-Mar-2013 09:25:46 (UTC+8)-
dc.identifier (Other Identifiers) G0099751010en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/57061-
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) 99751010zh_TW
dc.description (描述) 101zh_TW
dc.description.abstract (摘要) 近年來，面對傳統線性時間序列的預測問題，有許多技術上的改良而被大量廣泛的使用，但是線性模式往往無法處理常常發生結構改變(structural changes)的問題，這使得非線性(nonlinearity)時間序列轉折點的研究越來越受到重視，利用非線性時間序列解決實例更可以貼近真實情況。再者，隨著模糊理論的蓬勃發展以及區間軟計算(soft computing)的成熟，相較於點估計預測方法所需的嚴格假設，區間估計方法的假設寬鬆許多並且能符合實際情況，可以提供給決策者更彈性的選擇。本文將應用基因演算法(genetic algorithms)針對模糊區間資料(fuzzy data)作模糊分析(fuzzy analysis)，找出資料轉折的門檻區間(threshold interval)，藉此發展出非線性的區間門檻自迴歸模式(interval SETAR model)，最後以台股為例，建構出門檻自迴歸模型與傳統區間ARIMA模式比較，藉此探討其預測方法的效率評估與準確性。zh_TW
dc.description.abstract (摘要) In recent years, in the face of traditional linear time series forecasting problems, there are many technical improvements and widely used. But linear model are often unable to deal with the problem often happens structural changes, which makes the nonlinear turning point for the study of the time series more and more attention. Use nonlinear time series more close to the real situation. Moreover, with the fuzzy theories flourish and soft computing mature, compared to the point estimate methods required strict assumptions, interval estimation method which without many assumptions can meet the actual situation. It can be provided to decision-makers more flexibility of choice. In this paper, the application of genetic algorithms for fuzzy data to identify structural changes interval (threshold interval), so as to develop the nonlinear range threshold autoregressive mode (interval SETAR model), and finally, for example, the Taiwan stock market, construct a threshold autoregression model with the traditional interval ARIMA model to investigate the prediction method efficiency and accuracy.en_US
dc.description.tableofcontents 1.前言 1
2 研究理論與方法 3
2.1 門檻自迴歸模式 3
2.2 區間型門檻自迴歸模式 5
2.3 門檻區間自迴歸模式 7
2.4 基因演算法 8
2.5 模糊時間數列基因演算法 11
3.實證分析 13
3.1 資料分析 13
3.2 建構SETAR門檻區間 16
3.3 用外生多變數建構門檻轉換模式 18
4. 結論與建議 21		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0099751010en_US
dc.subject (關鍵詞) 非線性zh_TW
dc.subject (關鍵詞) 區間軟計算zh_TW
dc.subject (關鍵詞) 模糊分析zh_TW
dc.subject (關鍵詞) 基因演算法zh_TW
dc.subject (關鍵詞) 門檻自迴歸zh_TW
dc.subject (關鍵詞) 門檻區間zh_TW
dc.subject (關鍵詞) nonlinearen_US
dc.subject (關鍵詞) soft computingen_US
dc.subject (關鍵詞) fuzzy analysisen_US
dc.subject (關鍵詞) genetic algorithmsen_US
dc.subject (關鍵詞) SETARen_US
dc.subject (關鍵詞) threshold intervalen_US
dc.title (題名) 應用基因演算法決定SETAR門檻區間及其應用zh_TW
dc.title (題名) Use genetic algorithms to determine the SETAR threshold interval and Its Applicationsen_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1期，頁67-76。
程友梅(1995)，轉移型時間序列的認定。國立政治大學統計系碩士論文。
張新發(1996)，遺傳演算法在門檻自迴歸模式(d,r)值估計的應用。國立政治大學統計系碩士論文。
楊亦農(2009)，時間序列分析：經濟與財務上之應用，雙葉書廊，台北。
F.-M. Tseng and G.-H. Tzeng (2002) a fuzzy seasonal ARIMA model for forecasting. Fuzzy sets and systems, 126(3), 367-376.
H. T. Nguyen and B. Wu (2006) Fundamentals of Statistics with Fuzzy Data. New York：Springer.
Hansen, B.E. (1997). Inference in TAR Models, Studies in Nonlinear Dynamics and Econometrics, 2, 1-14.
Hsu, H.L. (2008). Evaluating forecasting performance for interval data. Computers and Mathematics with Applications 56, 2155-2163.
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.
Ludermir, T. B. (2008). Forecasting models for interval-valued time series. Neurocomputing 71, 3228-3238.
M. Bleaney, N. Gemmell, R.Kneller(1989) Testing the endogenous growth model: public expenditure, taxation, and growth over the long run.
M. Khashei, S.R. Hejazi and M. Bijari (2008) A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy sets and systems, 159, 769-786.
S.K. Chang (2007) On the Testing Hypotheses of Mean and Variance for Interval Data. Management Science & Statistical Decision, 4(2), 63-69.
Tong, H. & Lim, K. S. (1980). Journal of the Royal Statistical Society, Series B,"Threshold Autoregression, Limit Cycles and Cyclical Data (with discussion)", 42, 245-292.
Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press.
Tseng, F.M., Tseng, G.H., Yu, H.C., and Yuan, B.C. (2001). Fuzzy ARIMA model for forecasting the foreign exchange market. Fuzzy sets and systems 118(1), 9-19.
V. Kreinovich , H. T. Nguyen and B. Wu (2007) On-line algorithms for computing mean and variance of interval data, and their use in intelligent systems. Information Sciences, 177, 3228-3238.
Wu, B and Hung, S. (1999). Fuzzy Sets and Systems. A fuzzy identification procedure for nonlinear time series with example on ARCH and bilinear models. 108, 275-287.
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.
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