進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-0907201416580300
論文名稱(中文) 模糊時間序列之多因子隱藏式馬可夫預測模型
論文名稱(英文) A Multi-Factor HMM-Based Forecasting Model for Fuzzy Time Series
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系
系所名稱(英) Department of Industrial and Information Management
學年度 102
學期 2
出版年 103
研究生(中文) 張紋馨
研究生(英文) Wen-Shin Chang
學號 R36024037
學位類別 碩士
語文別 英文
論文頁數 62頁
口試委員 指導教授-李昇暾
口試委員-林清河
口試委員-耿伯文
口試委員-鄭亦君
中文關鍵字 模糊時間序列  預測  隱藏式馬可夫模型 
英文關鍵字 Fuzzy Time Series  Forecasting  Hidden Markov Model (HMM) 
學科別分類
中文摘要   現今企業經常會使用統計或數學模型等方式來分析歷史資料,希望藉此預測未來之趨勢或變化,良好的預測結果可以幫助企業增加獲利的機會,決策者也可以藉由預測結果做出適當的策略,以因應時代的變遷,是故良好且精確的預測方法常成為研究的主題。
  在過往的研究中有許多預測方法被提出,例如統計上的迴歸方程式、類神經網路等,都是經常引用及探討的預測方法,但以上的方式都是採用精確的數值資料以進行預測,對於約略之代表數值(如問卷之評分數值形式)或具有人類思維的語言變數便無法以傳統的方式加以計算分析,直到模糊理論的提出,才針對上述之模糊數值加以分析及探討,並提出相關的計算模型以解決此類資料之問題。模糊理論至今已廣泛應用在多個領域上,進而延伸出模糊時間序列預測模型,突破了傳統預測方法的限制,使得後續有不少學者致力於該研究領域上。
然而模糊時間序列預測模型在模糊關係的取得中,經常忽略遞移關係發生頻率的重要性,使得模糊關係在大型資料中解釋力不足,而且現有的預測模型往往是建構在有限的屬性資料預測,無法應用於多屬性資料。因此本研究提出基於模糊時間序列的多變數隱藏式馬可夫預測模型,此模型結合隱藏式馬可夫模型和模糊時間序列預測方法,並推展至多因子的預測。使用馬可夫模型的機率矩陣,能夠將遞移關係的頻率納入考量,進而降低傳統預測模型建立遞移關係時的計算量,並且考慮多因子的影響力,以提升預測的準確性。
英文摘要 Forecasting techniques are often applied to historical data in order to predict future trends. The ability to obtain more accurate forecasts can help policy-makers make more appropriate strategies to deal with future events, and this can also help businesses to increase their profits. Forecasting methods have received considerable attention in the literature.
Many widely-applied forecasting methods, like multiple-regression and artificial neural network approaches, are based on crisp data and lack the ability to deal with more ambiguous data, although this is very common in real-world problems. Researchers have thus proposed using fuzzy time series methods to deal with such data.
However, there remain some problems with the methods in the literature. First, the procedure of building fuzzy rules is complex and tedious. Second, traditional methods ignore the influence of rule frequency. Lastly, previous models cannot produce forecasts using data with multiple factors, even though most events of interest will be affected by many factors. In order to address these problems, this study presents a multi-factor hidden Markov forecasting model based on fuzzy time series. This model combines a hidden Markov model (HMM) with a framework of fuzzy time series forecasting, and utilizes more factors to get a better forecasting accuracy rate. The proposed method applies a fuzzy time series forecasting method to fuzzify historical data, and then constructs a set of multi-factor HMM-based relationships to predict the future trends of the data.
論文目次 Abstract V
摘要 VI
誌謝 VII
List of Tables VIII
List of Figures IX
CHAPTER 1 Introduction 1
1.1 Background and motivation 1
1.2 The goal and the contribution of this study 4
1.3 The structure of this study 4
CHAPTER 2 Literature Review 6
2.1 Fuzzy set theory 6
2.2 Fuzzy time series 8
2.2.1 Basic definition of fuzzy time series 8
2.2.2 Forecasting model of fuzzy time series 13
2.3 Hidden Markov model (HMM) 25
2.3.1 Three problems of hidden Markov model 27
2.3.2 Solution of three problems 27
CHAPTER 3 Model Development 31
3.1 Fuzzifying historical data 32
3.2 Building the HMM 34
3.2.1 Relative frequency method 36
3.2.2 Combinatorial method 36
3.3 Forecasting and defuzzification 40
CHAPTER 4 Experiment Results 42
4.1 Evaluation Indicators 42
4.2 Experiment I - weather of Alishan 43
4.2.1 Fuzzifying historical data 43
4.2.2 Building the multiple factor HMM model 45
4.2.3 Forecasting and defuzzifying 47
4.2.4 Evaluation and analysis 48
4.3 Experiment II - movie sales forecasting 51
4.3.1 Fuzzifying historical data 51
4.3.2 Building the HMM model 53
4.3.3 Forecasting and defuzzifying 55
4.3.4 Evaluation and analysis 56
CHAPTER 5 Conclusion and Future Work 58
Reference 60
參考文獻 Chen, S.-M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81(3), 311-319.
Chen, S.-M. (2002). Forecasting enrollments based on high-order fuzzy time series. Cybernetics and Systems, 33(1), 1-16.
Chen, S.-M., & Chen, C.-D. (2011). Handling forecasting problems based on high-order fuzzy logical relationships. Expert Syst. Appl., 38(4), 3857-3864.
Cheng, C.-H., Chang, J.-R., & Yeh, C.-A. (2006). Entropy-based and trapezoid fuzzification-based fuzzy time series approaches for forecasting IT project cost. Technological Forecasting and Social Change, 73(5), 524-542.
Cheng, C.-H., Chen, Y.-S., & Wu, Y.-L. (2009). Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model. Expert Syst. Appl., 36(2), 1826-1832.
Hsu, Y.-Y., Tse, S.-M., & Wu, B. (2003). A new approach of bivariate fuzzy time series analysis to the forecasting of a stock index. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(6), 671-690.
Huarng, K. (2001). Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets and Systems, 123(3), 387-394.
Huarng, K., & Yu, H.-K. (2006a). The application of neural networks to forecast fuzzy time series. Physica A: Statistical Mechanics and its Applications, 363(2), 481-491.
Huarng, K., & Yu, H.-K. (2006b). Ratio-based lengths of intervals to improve fuzzy time series forecasting. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 36(2), 328-340.
Hwang, J.-R., Chen, S.-M., & Lee, C.-H. (1998). Handling forecasting problems using fuzzy time series. Fuzzy Sets and Systems, 100(1–3), 217-228.
Joshi, B. P., & Kumar, S. (2012). Intuitionistic fuzzy sets based method for fuzzy time series forecasting. Cybernetics and Systems, 43(1), 34-47.
Lee, L.-W., Wang, L.-H., & Chen, S.-M. (2007). Temperature prediction and TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms. Expert Syst. Appl., 33(3), 539-550.
Lee, L.-W., Wang, L.-H., Chen, S.-M., & Leu, Y.-H. (2006). Handling forecasting problems based on two-factors high-order fuzzy time series. Fuzzy Systems, IEEE Transactions on, 14(3), 468-477.
Lewis, D. D. (1998). Naive (Bayes) at forty: The independence assumption in information retrieval. In C. Nédellec & C. Rouveirol (Eds.), Machine Learning: ECML-98 (Vol. 1398, pp. 4-15): Springer Berlin Heidelberg.
Li, S.-T., & Cheng, Y.-C. (2007). Deterministic fuzzy time series model for forecasting enrollments. Comput. Math. Appl., 53(12), 1904-1920. doi: 10.1016/j.camwa.2006.03.036
Li, S.-T., & Cheng, Y.-C. (2010). A Stochastic HMM-Based Forecasting Model for Fuzzy Time Series. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 40(5), 1255-1266.
Li, S.-T., Cheng, Y.-C., & Lin, S.-Y. (2008). A FCM-based deterministic forecasting model for fuzzy time series. Computers & Mathematics with Applications, 56(12), 3052-3063.
Miller, G. A. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. The Psychological Review, 63, 81-97.
Shah, M. (2012). Fuzzy based trend mapping and forecasting for time series data. Expert Syst. Appl., 39(7), 6351-6358.
Song, Q., & Chissom, B. S. (1993a). Forecasting enrollments with fuzzy time series - Part I. Fuzzy Sets and Systems, 54(1), 1-9.
Song, Q., & Chissom, B. S. (1993b). Fuzzy time-series and its models. Fuzzy Sets and Systems, 54(3), 269-277.
Song, Q., & Chissom, B. S. (1994). Forecasting enrollments with fuzzy time series - part II. Fuzzy Sets and Systems, 62(1), 1-8.
Stamp, M. (2004). A revealing introduction to hidden Markov models.
Sullivan, J., & Woodall, W. H. (1994). A comparison of fuzzy forecasting and Markov modeling. Fuzzy Sets and Systems, 64(3), 279-293.
Wang, M.-J. J., & Chang, T.-C. (1995). Tool steel materials selection under fuzzy environment. Fuzzy Sets and Systems, 72(3), 263-270.
Wang, N.-Y., & Chen, S.-M. (2009). Temperature prediction and TAIFEX forecasting based on automatic clustering techniques and two-factors high-order fuzzy time series. Expert Systems with Applications, 36(2, Part 1), 2143-2154.
Wong, W.-K., Bai, E., & Chu, A. W. (2010). Adaptive Time-Variant Models for Fuzzy-Time-Series Forecasting. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 40(6), 1531-1542.
Xiaolin, L., Parizeau, M., & Plamondon, R. (2000). Training hidden Markov models with multiple observations-a combinatorial method. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(4), 371-377.
Yu, H.-K. (2005). Weighted fuzzy time series models for TAIEX forecasting. Physica A: Statistical Mechanics and its Applications, 349(3–4), 609-624.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2024-12-31起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw