博碩士論文 974203015 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:9 、訪客IP:54.144.233.198
姓名 胡肇文(Jhao-Wun Hu)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 智慧型差分自回歸移動平均模型於時間序列預測之研究
(Intelligent ARIMA Approach to the Problem of Time Series Forecasting)
相關論文
★ 變數選擇在智慧型系統與應用之研究★ 智慧型系統之參數估測研究─一個新的DE方法
★ 合奏學習式智慧型系統在分類問題之研究★ 複數模糊類神經系統於多類別分類問題之研究
★ 融入後設認知策略的複數模糊認知圖於分類問題之研究★ 分類問題之研究-以複數型模糊類神經系統為方法
★ 計算智慧及複數模糊集於適應性影像處理之研究★ 智慧型模糊類神經計算模式使用複數模糊集合與ARIMA模型
★ Empirical Study on IEEE 802.11 Wireless Signal – A Case Study at the NCU Campus★ 自我建構式複數模糊ARIMA於指數波動預測之研究
★ 資料前處理之研究:以基因演算法為例★ 針對文字分類的支援向量導向樣本選取
★ 智慧型區間預測之研究─以複數模糊類神經、支持向量迴歸、拔靴統計為方法★ 複數模糊類神經網路在多目標財經預測
★ 智慧型模糊類神經計算使用非對稱模糊類神經網路系統與球型複數模糊集★ 複數型模糊類神經系統及連續型態之多蟻群演化在時間序列預測之研究
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 本研究使用類神經模糊系統並結合差分自回歸移動平均模型(ARIMA)與混合式學習法,提出一個創新性的計算智慧方法之於時間序列預測。依據上述方法,本研究設計出NFS-ARIMA模型做為一個適應性預測系統。在NFS-ARIMA預測系統中,重點在於模糊系統的設計並將差分自回歸移動平均模型應用到模糊法則的後鑑部。本研究使用兩種系統結構學習法來產生模糊法則並進行比較,分別為格狀法與自我組織分群法。在自我組織分群法中,透過輸入資料的分佈,NFS-ARIMA預測系統使用模糊C平均分裂演算法進行自我建構與學習以建立模糊法則。此外本研究結合粒子群最佳化演算法與遞迴最小平方估計法,形成複合式學習法進行系統參數學習,粒子群最佳化演算法用來調整模糊法則的前鑑部參數,至於遞迴最小平方估計法則用來調整後鑑部參數。本研究使用5個實驗範例來測試本系統的預測效能,透過實驗結果可證明本系統的預測準確性,且透過PSO-RLSE混合式學習法,NFS-ARIMA預測系統在訓練時可以達到極快的收斂速度並且具有極佳的預測效能,實驗1與實驗2的結果同時呈現本系統的預測準確度優於大部分其他文獻所提出的方法與模型並且有效提升預測準確度。而本系統也能針對現實世界中經濟與財務領域相關的時間序列,例如股票與國際匯率有不錯的預測效能與精準度。
摘要(英) A new computational intelligence approach to the problem of time series forecasting is proposed, using a Neuro-Fuzzy System (NFS), Auto-Regressive Integrated Moving Average (ARIMA) models and a novel hybrid learning method. The proposed intelligent system is denoted as the NFS-ARIMA model, which is used as an adaptive predictor to the problem of time series forecasting. For the NFS-ARIMA, the focus is on the formation of fuzzy If-Then rules, where ARIMA models are embedded in the consequent parts of If-Then rules. Two rule generation methods including grid-type and cluster-based self-organizing method are used to make comparison in system structure learning. For the cluster-based method, the NFS-ARIMA can learn its initial knowledge base from training data. With the mechanism of self-organization, fuzzy rules are generated by clusters using the FCM-based splitting algorithm (FBSA). For the hybrid learning method, the well-known particle swarm optimization (PSO) algorithm and the recursive least-squares estimator (RLSE) are combined in hybrid way so that they can update the free parameters of the NFS-ARIMA predictor in efficient way. The PSO is used to update the If-part parameters of the proposed predictor, and the RLSE is used to adapt the Then-part parameters. Five examples are used to test the proposed approach for forecasting ability. Through the experimental results, the proposed approach shows its excellence in prediction accuracy. With the hybrid PSO-RLSE learning method, the learning process for the NFS-ARIMA predictor can converge in fast pace, and the prediction accuracy is admirable. The results of Example 1 and 2 by the proposed approach are compared to other approaches. The performance comparison shows the proposed approach performs appreciably better than many compared approaches. The NFS-ARIMA forecasting approach is also applied to real-world applications of stock price and foreign exchange rate.
關鍵字(中) ★ 粒子群最佳化演算法
★ 複合式學習法
★ 自我組織
★ 分群
★ 差分自回歸移動平均模型
★ 遞迴最小平方估計法
★ 類神經模糊系統
★ 時間序列預測
關鍵字(英) ★ particle swarm optimization (PSO)
★ recursive least-squares estimator (RLSE)
★ neuro-fuzzy
★ time series forecasting
★ hybrid learning
★ cluster-based
★ ARIMA
★ self-organizing
論文目次 中文摘要 i
Abstract ii
致 謝 iii
目 錄 iv
圖目錄 v
表目錄 viii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 研究方法 4
1.4 論文架構 5
第二章 文獻探討 6
2.1 時間序列預測與ARIMA 模型 6
2.2 模糊理論與類神經模糊系統 9
2.3 模糊C平均分裂演算法 13
2.4 粒子群最佳化演算法 18
2.5 遞迴最小平方估計法 21
第三章 系統設計與架構 23
3.1 NFS-ARIMA預測系統設計 23
3.2 系統結構學習 27
3.3 系統參數學習 31
3.4 系統測試 32
第四章 實驗實作與結果 34
4.1 實驗1:Box-Jenkins瓦斯爐時間序列 34
4.2 實驗2:Mackey-Glass 混沌時間序列 44
4.3 實驗3:美元對台幣匯率時間序列 49
4.4 實驗4:Google股票日收盤價時間序列 55
4.5 實驗5:鴻海股票日收盤價時間序列 62
第五章 討論 68
第六章 結論 72
6.1 結論 72
6.2 未來研究方向 73
參考文獻 75
個人簡歷 80
參考文獻 [1] K. E. Parsopoulos and M. N. Vrahatis, "Particle swarm optimization method for constrained optimization problems," Intelligent Technologies–Theory and Application: New Trends in Intelligent Technologies, Frontiers in Artificial Intelligence and Applications, vol. 76, pp. 214-220, 2002.
[2] K. E. Parsopoulos and M. N. Vrahatis, "Recent approaches to global optimization problems through particle swarm optimization," Natural Computing, vol. 1, pp. 235-306, 2002.
[3] Y. Shi, R. C. Eberhart, E. Center, and I. N. Carmel, "Empirical study of particle swarm optimization," Proceedings of the 1999 Congress of Evolutionary Computation, vol. 3, pp.1945-1950, 1999.
[4] G. Venter and J. Sobieszczanski-Sobieski, "Multidisciplinary optimization of a transport aircraft wing using particle swarm optimization," Structural and Multidisciplinary Optimization, vol. 26, pp. 121-131, 2004.
[5] C. A. C. Coello, G. T. Pulido, and M. S. Lechuga, "Handling multiple objectives with particle swarm optimization," IEEE transactions on evolutionary computation, vol. 8, pp. 256-279, 2004.
[6] C. F. Juang, "A hybrid of genetic algorithm and particle swarm optimization for recurrent network design," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 34, pp. 997-1006, 2004.
[7] J. Kennedy and R. Eberhart, "Particle swarm optimization," IEEE International Conference on Neuro Networks, 1995, vol. 4, pp. 1942-1948, 1995.
[8] Y. Shi and R. C. Eberhart, "Parameter selection in particle swarm optimization," Lecture Notes in Computer Science, vol. 1447, pp. 591-600, 1998.
[9] L. A. Zadeh, "Fuzzy sets," Information and Control, vol. 8, pp. 338-353, 1965.
[10] L. A. Zadeh, "Fuzzy logic and approximate reasoning," Synthese, vol. 30, pp. 407-428, 1975.
[11] G. J. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall, Upper Saddle River, NJ, USA, 1995.
[12] S. Fukami, M. Mizumoto, and K. Tanaka, "Some considerations on fuzzy conditional inference," Fuzzy Sets and Systems, vol. 4, pp. 243-273, 1980.
[13] J. S. R. Jang, C. T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing: a Computational Approach to Learning and Machine Intelligence, Prentice Hall, Upper Saddle River, NJ, USA, 1997.
[14] M. Sugeno and G. T. Kang, "Structure identification of fuzzy model," Fuzzy Sets and Systems, vol. 28, pp. 15-33, 1988.
[15] L. J. Herrera, H. Pomares, I. Rojas, A. Guillen, J. Gonzalez, M. Awad, and A. Herrera, "Multigrid-based fuzzy systems for time series prediction: CATS competition," Neurocomputing, vol. 70, pp. 2410-2425, 2007.
[16] H. J. Rong, N. Sundararajan, G. B. Huang, and P. Saratchandran, "Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction," Fuzzy Sets and Systems, vol. 157, pp. 1260-1275, 2006.
[17] J. S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, pp.665-685, 1993.
[18] I. Sugiarto and S. Natarajan, "Parameter estimation using least square method for MIMO Takagi-Sugeno neuro-fuzzy in time series forecasting," Jurnal Teknik Elektro, vol. 7, pp. 82-87, 2008.
[19] M. Zounemat-Kermani and M. Teshnehlab, "Using adaptive neuro-fuzzy inference system for hydrological time series prediction," Applied Soft Computing, vol. 8, pp. 928-936, 2008.
[20] K. B. Cho and B. H. Wang, "Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction," Fuzzy Sets and Systems, vol. 83, pp. 325-339, 1996.
[21] D. Nauck and R. Kruse, "Neuro-fuzzy systems for function approximation," Fuzzy Sets and Systems, vol. 101, pp. 261-272, 1999.
[22] S. Paul and S. Kumar, "Subsethood-product fuzzy neural inference system (SuPFuNIS)," IEEE Transactions on Neural Networks, vol. 13, pp. 578-599, 2002.
[23] J. Kim and N. Kasabov, "HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems," Neural Networks, vol. 12, pp. 1301-1319, 1999.
[24] S. Chen, C. F. N. Cowan, and P. M. Grant, "Orthogonal least squares learning algorithm for radial basis function networks," IEEE Transactions on Neural Networks, vol. 2, pp. 302-309, 1991.
[25] Y. Chen, B. Yang, and J. Dong, "Time-series prediction using a local linear wavelet neural network," Neurocomputing, vol. 69, pp. 449-465, 2006.
[26] Y. Chen, B. Yang, J. Dong, and A. Abraham, "Time-series forecasting using flexible neural tree model," Information Sciences, vol. 174, pp. 219-235, 2005.
[27] N. K. Kasabov, J. Kim, M. J. Watts, and A. R. Gray, "FuNN/2 - A Fuzzy Neural Network Architecture for Adaptive Learning and Knowledge Acquisition," Information Sciences, vol. 101, pp. 155-175, 1997.
[28] L. Zhao and Y. Yang, "PSO-based single multiplicative neuron model for time series prediction," Expert Systems with Applications, vol. 36, pp. 2805-2812, 2009.
[29] X. Deng, X. Wang, "Incremental learning of dynamic fuzzy neural networks for accurate system modeling", Fuzzy Sets and Systems, vol.160, pp.972–987, 2009.
[30] Y. Gao and M. J. Er, "NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches," Fuzzy Sets and Systems, vol. 150, pp. 331-350, 2005.
[31] I. Rojas, O. Valenzuela, F. Rojas, A. Guillen, L. J. Herrera, H. Pomares, L. Marquez, and M. Pasadas, "Soft-computing techniques and ARMA model for time series prediction," Neurocomputing, vol. 71, pp. 519-537, 2008.
[32] L. Cao, "Support vector machines experts for time series forecasting," Neurocomputing, vol. 51, pp. 321-339, 2003.
[33] A. Sfetsos and C. Siriopoulos, "Time series forecasting with a hybrid clustering scheme and pattern recognition," IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 34, pp. 399-405, 2004.
[34] G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis: Forecasting and Control: Holden-day, San Francisco, CA, USA, 1976.
[35] J. C. Dunn, "A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters," Cybernetics and Systems, vol. 3, pp. 32-57, 1973.
[36] J. C. Bezdek, "Pattern Recognition with Fuzzy Objective Function Algorithms", Plenum Press, New York, USA, 1981.
[37] H. Sun, S. Wang, and Q. Jiang, "FCM-based model selection algorithms for determining the number of clusters," Pattern recognition, vol. 37, pp. 2027-2037, 2004.
[38] M. Ramze Rezaee, B. P. F. Lelieveldt, and J. H. C. Reiber, "A new cluster validity index for the fuzzy c-mean," Pattern Recognition Letters, vol. 19, pp. 237-246, 1998.
[39] C. J. Lin and Y. J. Xu, "A self-adaptive neural fuzzy network with group-based symbiotic evolution and its prediction applications," Fuzzy Sets and Systems, vol. 157, pp. 1036-1056, 2006.
[40] C. Li and C. Y. Lee, "Self-organizing neuro-fuzzy system for control of unknown plants," IEEE Transactions on Fuzzy Systems, vol. 11, pp. 135-150, 2003.
[41] M. Y. Chen and D. Linkens, "A systematic neuro-fuzzy modeling framework with application to material property prediction," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 31, pp. 781-790, 2001.
[42] A. F. Gomez-Skarmeta, M. Delgado, and M. A. Vila, "About the use of fuzzy clustering techniques for fuzzy model identification," Fuzzy Sets and Systems, vol. 106, pp. 179-188, 1999.
[43] D. Graves and W. Pedrycz, "Fuzzy prediction architecture using recurrent neural networks," Neurocomputing, vol. 72, pp. 1668-1678, 2009.
[44] Yahoo! Finance website,
http://ichart.finance.yahoo.com/table.csv?s=GOOG&a=02&b=08&c=2006&d=01&e=25&f=2010&g=d&ignore=.csv.
[45] OANDA, http://www.oanda.com/currency/historical-rates.
[46] 台灣證券交易所, http://www.twse.com.tw/ch/trading/exchange/STOCK_DAY_AVG/STOCK_DAY_AVGMAIN.php
指導教授 李俊賢(Chunshien Li) 審核日期 2010-7-26
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明