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| Title: | 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 |
| Authors: | 江增堂 |
| Contributors: | 吳柏林
Wu, Berlin
江增堂 |
| Keywords: | 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 |
| Date: | 2012 |
| Issue Date: | 2013-03-01 09:25:46 (UTC+8) |
| Abstract: | 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. |
| Reference: | 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. |
| Description: | 碩士
國立政治大學
應用數學研究所
99751010
101 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0099751010 |
| Data Type: | thesis |
| Appears in Collections: | [應用數學系] 學位論文
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