跳到主要內容

臺灣博碩士論文加值系統

(44.220.44.148) 您好!臺灣時間:2024/06/18 15:20
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:劉仲琦
研究生(外文):Chung-Chi Liu
論文名稱:基於不同離散化方法之加權高階模糊時間序列模式
論文名稱(外文):High-Order Weighted Fuzzy Time Series Based on Different Discretization Approach
指導教授:張景榮張景榮引用關係
指導教授(外文):Jing-Rong Chang
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:資訊管理系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:95
中文關鍵詞:決策支援系統N分位離散化方法變動長度離散化方法
外文關鍵詞:decision support systemN-th quantile discretization approachvariable length discretization approach
相關次數:
  • 被引用被引用:2
  • 點閱點閱:317
  • 評分評分:
  • 下載下載:8
  • 收藏至我的研究室書目清單書目收藏:0
人類社會中存在許多不確定性的問題,例如經濟成長率、財務危機的預測等。自從Song 和 Chissom 兩位學者在1993年提出模糊時間序列的觀念後,許多學者先後提出不同的模式來處理這些問題。然而先前的研究在模糊化的過程中,通常僅依據主觀意見進行模糊語意的離散化,因此較不能夠客觀地反應資料集的特性。有鑑於此,這項研究的方向主要是探討在模糊時間序列中,如何客觀的決定各個區間的長度以及語意的數量。本研究提出變動長度離散化方法(Variable Length Discretization Approach, VLDA)及N分位離散化方法(N-th Quantile Discretization Approach, NQDA)[26]來結合高階加權模糊時間序列,希望這兩個模式能解決先前方法的問題。最後,為了驗證本研究提出之方法,將採用台灣證券交易所(Taiwan Stock Exchange Corporation)提供的台灣股價加權指數(Taiwan Stock Exchange Capitalization Weighted Stock Index, TAIEX)為本研究績效評估的實驗資料集,並且針對不同的績效指標納入近年其它的研究模式與本研究做比較,結果顯示本研究之預測能力有進一步的改善。此外,本研究將實際開發一套證卷市場的智慧型決策支援系統(Decision Support System, DSS),提供投資大眾未來在面對股票市場投資時,一個有用的輔助決策支援工具。
There are many uncertainty problems in the Human society, such as the forecasting of economic growth rate, financial crisis, etc. Since Song and Chissom proposed the concept of fuzzy time series in 1993, many scholars have proposed different models to deal with these problems. However, previous studies usually did not consider the transfer original data to the fuzzy linguistic value by the subjective opinions in fuzzy process, which cannot objectively show the characteristics of the data. Based on above concepts, the purpose of this study is to explore ways of determining the objective lengths of intervals and amount of linguistic in fuzzy time series. This study proposed a high-order weighted fuzzy time series model based on variable length discretization approach (VLDA) and N-th quantile discretization approach (NQDA) to make forecasts. In order to verify the proposed method, the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) from the Taiwan Stock Exchange Corporation are used in the experiment, and the experiment results are compared with other methods in with this study. The forecasting performance shows that the proposed method having better forecasting ability. An intelligent decision support system (DSS) for stock market will be developed in this study. It is supposed to be a useful decision support tools for the investor to make better trading strategies in the future stock market.
第一章 緒論 ................................................................................................. 1
1. 1 研究背景 ............................................................................................... 1
1. 2 研究動機 ............................................................................................... 2
1. 3 研究目的 ............................................................................................... 4
1. 4 研究範圍與限制 ................................................................................... 6
1. 5 研究流程與架構 ................................................................................... 7
第二章 文獻探討 ......................................................................................... 9
2. 1 模糊集合論(Fuzzy Set Theory) ............................................................... 9
2.1.1 傳統集合與模糊集合 ......................................................................... 9
2.1.2 歸屬函數與基本運算 ....................................................................... 10
2.1.3 語意變數 ........................................................................................... 12
2.1.4 模糊關係 ........................................................................................... 14
2. 2 模糊時間序列 ..................................................................................... 14
2.2.1 模糊時間序列基本概念 ................................................................... 14
2.2.2 模糊時間序列之相關研究 ............................................................... 16
2. 3 基因演算法(Genetic Algorithm) .......................................................... 24
2.3.1 複製(Reproduction)運算 ............................................................. 24
2.3.2 交配(Crossover)運算 ................................................................... 26
2.3.3 突變(Mutation)運算 .................................................................... 27
2.3.4 基因演算法結合模糊時間序列之相關研究 ................................... 27
2. 4 變動長度基因演算法(Variable Length Genetic Algorithm) ................. 30
2.4.1 交配(Crossover)運算 ............................................................................ 30
2.4.2 插入(Insertion)運算 .......................................................................... 31
2.4.3 刪除(Deletion)運算 .............................................................................. 31
第三章 研究方法 ....................................................................................... 33
3. 1 模糊時間序列基礎流程 ..................................................................... 33
3. 2 本研究提出之加權高階模糊時間序列模式 ...................................... 33
3.2.1 資料前處理 ....................................................................................... 34
3.2.2 基於變動長度離散化方法(VLDA)之加權高階時間序列模式 ....... 34
3.2.3 基於N分位離散化方法(NQDA)之加權高階時間序列模式 ..... 44
第四章 實驗數據與分析 ........................................................................... 47
4. 1 資料來源與說明 ................................................................................. 47
4. 2 模式實證 ............................................................................................. 49
4.2.1 資料預前處理 ................................................................................... 49
4.2.2 變動長度離散化方法(VLDA)模式驗證 ............................................ 50
4.2.3 N分位離散化方法(NQDA)模式驗證 ............................................. 61
4.2.4 投資理財模擬買賣模式................................................................... 62
4. 3 實驗結果與分析 ................................................................................. 63
4.3.1 考慮前期誤差之N分位離散化方法(NQDA)績效比較 .................. 64
4.3.2 變動(variable)長度與固定(invariable)長度離散化方法之比較 ...... 68
4.3.3 近年其它模式之績效比較 ............................................................... 71
4. 4 實驗環境與參數設定 ......................................................................... 79
4. 5 模糊股市投資預測系統 ................................................................. 81
第五章 結論 ............................................................................................. 84
5. 1 實驗結果討論 ..................................................................................... 84
5. 2 研究貢獻 ............................................................................................. 87
5. 3 未來研究 ............................................................................................. 88
參考文獻 ..................................................................................................... 89
附錄 ............................................................................................................. 94

圖1.1 研究架構圖 ....................................................................................... 8
圖2.1 連續型歸屬函數表示法 ................................................................. 11
圖2.2 三角形歸屬函數表示圖 ................................................................. 11
圖2.3 梯形歸屬函數表示圖 ..................................................................... 12
圖2.4 五等尺度之語意變數表示圖 ......................................................... 13
圖2.5 傳統基因演算法流程圖 ................................................................. 24
圖3.1 本研究模式階段流程圖 ................................................................. 33
圖3.2 變動長度離散化方法演化架構 ..................................................... 35
圖3.3 初始染色體示意圖 ......................................................................... 36
圖3.4 VLDA 交配(Crossover)運算示意圖 ................................................. 40
圖3.5 VLDA 數值突變運算示意圖 ........................................................... 41
圖3.6 VLDA 刪除(Deletion)運算示意圖 ................................................... 43
圖4.1 2003年TAIEX 之收盤價原始指數資料走勢圖 ........................... 48
圖4.2 模糊歸屬函數與染色體編碼示意圖 ............................................. 50
圖4.3 交配運算實際舉例示意圖 ............................................................. 57
圖4.4 數值突變運算實際舉例示意圖 ..................................................... 58
圖4.5 插入(Insertion)運算實際舉例示意圖 ............................................ 58
圖4.6 刪除(Deletion)運算實際舉例示意圖 ............................................. 59
圖4.7 TAIEX 2003年三階VLDA 最佳染色體與模糊歸屬函數圖 ............ 60
圖4.8 2003年TAIEX 之原始指數與最佳染色體三階預測值 ................. 60
圖4.9 2003年TAIEX 之收盤價原始指數與NQDA 三階預測值 ............ 62
圖4.10 模擬買賣模糊歸屬函數示意圖 ................................................... 63
圖4.11 考慮前期誤差與不考慮前期誤差之平均均方根差(RMSE)比較 ... 67
圖4.12 考慮前期誤差與不考慮前期誤差之平均方向正確率(DA)比較 ... 67
圖4.13 考慮前期誤差與不考慮前期誤差之平均投資報酬率(ROI)比較 ... 67
圖4.14 股市投資預測支援系統整體架構圖 ........................................... 83
附圖1 「智慧型股市投資預測支援系統」首頁畫面 ............................ 94
附圖2 資料匯入畫面 ................................................................................ 94
附圖3 股市投資交易買賣警訊 ................................................................ 95
附圖4 模式績效評估畫面 ........................................................................ 95

表2.1 五等尺度之語意變數與三角模糊數對照表 ................................. 14
表2.2 近年模糊時間序列相關文獻 ......................................................... 20
表2.3 近年基因演算法結合模糊時間序列模式相關文獻 ..................... 29
表4.1 2003年TAIEX原始收盤價指數資料 .............................................. 48
表4.2 2003年TAIEX原始收盤價指數之漲跌幅資料 .............................. 49
表4.3 隨機產生之初始族群舉例 ............................................................. 51
表4.4 以Chromosome1 模糊化TAIEX 2003歷史資料舉例 ................... 52
表4.5 以Chromosome1 建立TAIEX 2003 一階模糊關係 ..................... 54
表4.6 以Chromosome1 合併後的模糊關係與權重 ............................... 55
表4.7 以Chromosome1 建立模糊語意值與反模糊化值 ....................... 55
表4.8 TAIEX 2003年VLDA 最佳染色體預測結果及預測誤差舉例 ......... 59
表4.9 NQDA 採用不同語意數量之預測績效比較 ................................... 65
表4.10 NQDA 採用原始資料型態與動差資料型態之RMSE 與DA 比較.66
表4.11 NQDA 採用原始資料型態與動差資料型態之ROI 比較 ............ 66
表4.12 固定(invariable)長度離散化方法之均方根差(RMSE)比較 ........ 69
表4.13 固定(invariable)長度離散化方法之方向正確率(DA)比較 ..... 69
表4.14 固定(invariable)長度離散化方法之投資報酬率(ROI)比較 .... 70
表4.15 變動(variable)與固定(invariable)長度模式之均方根差(RMSE)比較 ... 70

表4.16 變動(variable)與固定(invariable)長度模式之方向正確率(DA)比較 ... 70
表4.17 變動(variable)與固定(invariable)長度模式之投資報酬率(ROI)比較 ... 71
表4.18 2000~2004年TAIEX之不同模式預測均方根差(RMSE)比較 ....... 74
表4.19 2000~2004年TAIEX之不同模式投資報酬率(ROI)比較 ............... 75
表4.20 2000~2004年TAIEX之不同模式預測方向正確率(DA)績效比較 ... 75
表4.21 近年TAIEX 之不同模式預測均方根差(RMSE)比較 .............. 76
表4.22 近年TAIEX 之不同模式預測投資報酬率(ROI)比較 ............. 76
表4.23 近年TAIEX 之不同模式預測預測方向正確率(DA)比較 ............ 77
表4.24 VLDA模式於TAIEX 2000 ~2004年最佳語意個數 ........................ 77
表4.25 VLDA模式於TAIEX 2010 ~2012年最佳語意個數 ........................ 78
表4.26 近年TAIEX 之不同模式運算執行時間比較(單位:秒) ............... 78
表4.27 近年不同模式之特性比較 ........................................................... 79
表4.28 實驗環境軟硬體相關資訊 ........................................................... 79
表4.29 VLDA 模式相關參數設定 .............................................................. 80
表4.30 軟硬體需求環境 ........................................................................... 81
[1]王文俊(2007),認識Fuzzy,第三版,全華圖書出版,台北。
[2]林昇甫(2009),徐永吉,基因演算法及其應用,五南,台北。
[3]楊奕農(2009),時間序列分析:經濟與財務上之應用,第二版,雙葉書廊出版,台北。
[4]蘇木春、張孝德(2009),機器學習 : 類神經網路、模糊系統以及基因演算法則,修訂二版,全華科技圖書股份有限公司出版,臺北。
[5]張景榮、廖淑瑩、黃煜傑(2010),「基於不同歸屬程度之加權模糊時間序列模式」,第十五屆人工智慧與應用研討會,新竹,台灣,第226-231頁。
[6]張景榮、黃煜傑(2010),「結合模糊聚類與不同門檻程度之加權模糊時間序列模式」,第二十二屆人工智慧與應用研討會,台中,台灣,第74頁。
[7]B. P. Joshi, and S. Kumar(2012), “Intuitionistic Fuzzy Sets based Method for Fuzzy Time Series Forecasting, “ Cybernetics and Systems: An International Journal, Vol. 43, No. 1, pp. 34-47.
[8]C. C. Tsai, and S. J. Wu(2000), “Forecasting Enrolments with High-order Fuzzy Time Series,” Proceedings of 19th International Conference on Fuzzy Information Processing Society, North American, pp. 196-200.
[9]C. H. Aladag(2013), “Using Multiplicative Neuron Model to Establish Fuzzy Logic Relationships,” Expert Systems with Applications, Vol. 40, No. 3, pp. 850-853.
[10]C. H. Aladag, U. Yolcu, and E. Egrioglu(2010), “A High Order Fuzzy Time Series Forecasting Model Based on Adaptive Expectation and Artificial Neural Networks, “ Mathematics and Computers in Simulation, Vol. 81, No. 4, pp. 875-882.
[11]J. R. Chang, C. H. Huang, C. C. Liu, and Y. J. Huang(2010), “A Stock Market Forecasting Support System Based on Weighted and High-Order Fuzzy Time Series,” The 16th Conference on Information Management & Practice, Yunlin, Taiwan.
[12]C. H. Cheng, J. R. Chang, C. Y. Kuo, and S. Y. Liao(2009), “A Fuzzy Quantitative Integrated Metric Model for CMMI Appraisal,” Lecture Notes in Artificial Intelligence, Vol. 5572, pp. 219-226.
[13]C. H. Cheng, J. R., Chang, and C. A. Yeh(2006), “Entropy-based and Trapezoid Fuzzification-based Fuzzy Time Series Approaches for Forecasting IT Project Cost,” Technological Forecasting and Social Change, Vol. 73, No.5 , pp. 524-542.
[14]C. H. Cheng, L. Y. Wei, J. W. Liu, and T. L. Chen(2013), “OWA-based ANFIS Model for TAIEX Forecasting,” Expert Systems with Applications, Vol. 30, pp. 442-448.
[15]C. H. Cheng, T. L. Chen, H. J. Teoh, and C. -H. Chiang(2008), “Fuzzy Time-Sries based on Adaptive Expectation Model for TAIEX Forecasting,” Expert Systems with Applications, Vol. 34, pp. 1126-1132.
[16]C. H. Cheng, Y. S. Chen, and Y. L. Wu(2009), “Forecasting Innovation Diffusion of Productsusing Trend-Weighted Fuzzy Time-Series Model,” Expert Systems with Applications, Vol. 36, pp. 1826-1832.
[17]C. H. Su, C. H. Cheng, and W. L. Tsai(2013), “Fuzzy Time Series Model Based on Fitting Function for Forecasting TAIEX Index,” International Journal of Hybrid Information Technology, Vol. 6, No. 1, pp. 111-122.
[18]E. Bai, W. K. Wong, W. C. Chu, M. Xia, and F. Pan(2011), “A Heuristic Time-Invariant Model for Fuzzy Time Series Forecasting,” Expert Systems with Applications, vol. 38, No. 3, pp. 2701-2707.
[19]E. Bulut, O. Duru, and S. Yoshida(2012), “Exponential Length of Intervals for Fuzzy Time Series Forecasting,” Computational Intelligence for Financial Engineering and Economics, pp. 1-6.
[20]E. Egrioglu, C. H. Aladag, and U. Yolcu(2013), “Fuzzy Time Series Forecasting with A Novel Hybrid Approach Combining Fuzzy C-Means and Neural Networks,” Expert Systems with Applications, Vol. 40, No. 3, pp. 854-857.
[21]E. Egrioglu, C. H. Aladag, U. Yolcu, V. R. Uslu, and M. A. Basaran(2009), “A New Approach based on Artificial Neural Networks for High Order Multivariate Fuzzy Time Series, “ Expert Systems with Applications, Vol. 36, No. 7, pp. 10589-10594.
[22]G. P. Zhang(2004), “Business Forecasting with Artificial Neural Networks: An Overview,” Idea Group Publishing: neural networks in business forecasting, pp. 1-22.
[23]H. J. Teoh, T. L. Chen, C. H. Cheng, and H. H. Chu(2009), “A Hybrid Multi-Order Fuzzy Time Series for Forecasting Stock Markets,” Expert Systems with Applications, Vol. 36, No. 4, pp. 7888-7897.
[24]J. Holland(1975), “Adaptation in Natural and Artificial Systems, “ University of Michigan Press.
[25]J. I. Park, D. J. Lee, C. K. Song, and M. G. Chun(2010), “TAIFEX and KOSPI 200 Forecasting based on Two-Factors High-Order Fuzzy Time Series and Particle Swarm Optimization,” Expert Systems with Applications, Vol. 37, No. 2, pp. 959-967.
[26]J. R. Chang, and C. C. Liu(2013), “A Fuzzy Time Series model based on N-th Quantile Discretization Approach for TAIEX Forecasting,” 2013 5th International Conference on Knowledge and Smart Technology, Chonburi, Thailand, pp. 5-10.
[27]J. R. Chang, and Y. J. Huang(2011), “A weighted fuzzy time series model based on adoptive OWA operators,” Proceedings of 1th International Conference on Uncertainty Reasoning and Knowledge Engineering, pp. 94-97.
[28]J. R. Chang, L. Y. Wei, and Cheng, C. H.(2011), “A Hybrid ANFIS Model based on AR and Volatility for TAIEX Forecasting,” Applied Soft Computing, Vol. 11, No. 1, pp. 1388-1395.
[29]J. R. Chang, Y. T. Lee, S. Y. Liao, and C. H. Cheng(2007), “Cardinality-based Fuzzy Time Series for Forecasting Enrollments, “ Lecture Notes in Artificial Intelligence, Vol. 4570, pp. 735-744.
[30]K. Huarng, and H. K. Yu(2005), “A Type 2 Fuzzy Time Series Model for Stock Index Forecasting,” Physica A: Statistical Mechanics and its Applications, Vol. 353, pp. 445-462.
[31]K. Huarng, H. K. Yu, and Y. W. Hsu(2007), “A Multivariate Heuristic Model for Fuzzy Time-Series Forecasting,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, Vol. 37, No. 4, pp. 836-846.
[32]L. A. Zadeh(1965), “Fuzzy Sets. Information and Control,” Vol. 8, No. 3, pp. 338-353.
[33]L. A. Zadeh(1975a), “The Concept of A Linguistic Variable and Its Application to Approximate Reasoning-I,” Information Sciences, Vol. 8, No. 3, pp. 199-249.
[34]L. A. Zadeh(1975b), “The Concept of A Linguistic Variable and Its Application to Approximate Reasoning-II,” Information Sciences, Vol. 8, No. 4, pp. 301-357.
[35]L. A. Zadeh(1976), “The Concept of A Linguistic Variable and Its Application to Approximate Reasoning-III,” Information Sciences, Vol. 9, No. 1, pp. 43-80.
[36]L. Wang, X. Liu, and W. Pedrycz(2013), “Effective Intervals Determined by Information Granules to Improve Forecasting in Fuzzy Time Series,” Expert Systems with Applications, Vol. 40, pp. 5673-5679.
[37]L. W. Lee, L. H. Wang, and S. M. Chen(2007), “Temperature Prediction and TAIFEX Forecasting based on Fuzzy Logical Relationships and Genetic Algorithms,” Expert Systems with Applications, Vol. 34, No. 1, pp. 328-336.
[38]L. W. Lee, L. H. Wang, and S. M. Chen (2008), “Temperature Prediction and TAIFEX Forecasting based on High-Order Fuzzy Logical Relationships and Genetic Simulated Annealing Techniques,” Expert Systems with Applications, Vol. 34, No. 1, pp. 328-336.
[39]L. W. Lee, and S. M. Chen(2004), “Temperature prediction using genetic algorithms and fuzzy time series,” In Proceedings of the 2004 International conference on Information Management, Miaoli, Taiwan, pp. 299-306.
[40]L. Y. Wei(2012), “An Adaptive Expectation Genetic Algorithm based on Anfis and Multinational Stock Market Volatility Causality for TAIEX Forecasting,” Cybernetics and Systems: An International Journal, Vol. 43, pp. 410-425.
[41]M. Shah(2012), “Fuzzy based Trend Mapping and Forecasting for Time Series Data,” Expert Systems with Applications, Vol. 39, pp. 6351-6358.
[42]N. Y. Wang, and S. M. Chen(2009), “Temperature Prediction and TAIFEX Forecasting based on Automatic Clustering Techniques and Two-Factors High-Order Fuzzy Time Series,” Expert Systems with Applications , “ Vol. 36, pp. 2143-2154.
[43]Q. Song, and B. S. Chissom(1993a), “Fuzzy Time Series and Its Models,” Fuzzy Sets and Systems, Vol. 54, No. 3, pp. 269-277.
[44]Q. Song, and B. S. Chissom(1993b), “Forecasting Enrollments with Fuzzy Time Series - Part I,” Fuzzy Sets and Systems, Vol. 54, No. 1, pp. 1-10.
[45]Q. Song, and B. S. Chissom(1994), “Forecasting Enrollments with Fuzzy Time Series - Part II,” Fuzzy Sets and Systems, Vol. 62, No. 1, pp. 1-8.
[46]R. Srikanth(1995), “A Variable-Length Genetic Algorithm for Clustering and Classification, “ Pattern Recognition Letters, Vol. 16, pp. 789-800
[47]S. M. Chen(1996), “Forecasting Enrollments based on Fuzzy Time Series,” Fuzzy sets and systems, Vol. 81, No. 3, pp. 311-319.
[48]S. M. Chen, and C. D. Chen(2011), “Handling Forecasting Problems Based on High-Order Fuzzy Logical Relationships,” Expert Systems with Applications, Vol. 38, pp. 3857-3864.
[49]S. M. Chen, and C. D. Chen(2011), “TAIEX Forecasting Based on Fuzzy Time Series and Fuzzy Variation Groups, Institute of Electrical and Electronics Engineers,” IEEE Transactions on Fuzzy Systems, Vol. 19, Iss. 1, pp. 1-12.
[50]S. M. Chen, and J. R. Hwang(2007), “Temperature Prediction Using Fuzzy Time Series, “ IEEE Transactions on Systems, Man and Cybernetics, Part B, Vol. 30, No. 2, pp. 263-275.
[51]S. M. Chen, and K. Tanuwijaya(2011), “Multivariate Fuzzy Forecasting based on Fuzzy Time Series and Automatic Clustering Techniques, “ Expert Systems with Applications, Vol. 38, No. 8, pp. 10594-10605.
[52]S. M. Chen, and N. Y. Chung(2006), “Forecasting Enrollments Using High-Order Fuzzy Time Series and Genetic Algorithms,” International Journal of Intelligent Systems, Vol. 21, pp. 485–501.
[53]S. Venkadesh, G. Hoogenboom, W. Potter, and R. McClendon(2013), “ A Genetic Algorithm to Refine Input Data Selection for Air Temperature Prediction Using Artificial Neural Networks,” Expert Systems with Applications, Vol. 13, pp. 2253-2260.
[54]Taiwan Stock Exchange Corporation, http://www.twse.com.tw.
[55]T. A. Jilani, U. Amjad, J. Jaafar, and S. Hassan(2012), “An improved heuristic-based fuzzy time series forecasting model using genetic algorithm,” International Conference on Computer and Information Science, vol. 1, pp. 242-247.
[56]T. A. Jilani, U. Amjad, and N. Mastorakis(2012), “A Hybrid Genetic Algorithm and Particle Swarm Optimization based Fuzzy Times Series Model for TAIFEX and KSE-100 Forecasting,” Recent Researches in Applied Information Science, pp. 212-218.
[57]T. H. K. Yu(2005), “Weighted Fuzzy Time Series Models for TAIEX Forecasting,” Physica A: Statistical Mechanics and its Applications, Vol. 349, No. 3-4, pp. 609-624.
[58]T. H. K. Yu, and K. H. Huarng(2008), “A Bivariate Fuzzy Time Series Model to Forecast the TAIEX,” Expert Systems with Applications, Vol. 34, No. 4, pp. 2945-2952.
[59]T. H. K. Yu, and K. H. Huarng(2010), “A Neural Network-based Fuzzy Time Series Model to Improve Forecasting,” Expert Systems with Applications, vol. 37, pp. 3366-3372.
[60]T. H. K. Yu, and K. H. Huarng(2010), “Corrigendum to A Bivariate Fuzzy Series Model to Forecast the TAIEX,” Expert Systems with Applications, Vol. 37, No. 7, pp. 5529.
[61]T. J. Ross(2006), Fuzzy Logic with Engineering Applications (2ed.), New York: McGraw-Hill.
[62]U. Amjad, T. A. Jilani, and F. Yasmeen(2012), “A Two Phase Algorithm for Fuzzy Time Series Forecasting Using Genetic Algorithm and Particle Swarm Optimization Techniques, “ International Journal of Computer Applications, Vol. 55, No.16, pp. 975-8887.
[63]U. Yolcu, E. Egrioglu, V. R. Uslu, M. A. Basaran, and C. H. Aladag(2009), “A New Approach for Determining the Length of Intervals for Fuzzy Time Series, “ Applied Soft Computing, Vol. 9, No. 2, pp. 647-651.
[64]Y. Leu, C. P. Lee, and Y. Z. Jou(2009), “A Distance-based Fuzzy Time Series Model for Exchange Rates Forecasting,” Expert Systems with Applications, Vol. 36, pp. 8107-8114.
[65]Z. Ismail, and R. Efendi(2011), “Enrollment Forecasting Based on Modified Weight Fuzzy Time Series,” Journal of Artificial Intelligence, Vol. 4, No. 1, pp. 110-118.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top