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 U0026-1906201314433400
論文名稱(中文) 最短鄰近階數之高階模糊時間序列預測模式
論文名稱(英文) Forecasting High Order Fuzzy Time Series with Minimum Recent orders
校院名稱 成功大學
系所名稱(中) 資訊管理研究所
系所名稱(英) Institute of Information Management
學年度 101
學期 2
出版年 102
研究生(中文) 黃鑫亮
研究生(英文) Shin-Liang Huang
學號 r76001053
學位類別 碩士
語文別 英文
論文頁數 60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-郭淑靜
中文關鍵字 模糊時間序列  模糊邏輯關係  預測  KNN 
英文關鍵字 fuzzy time series  fuzzy logical relationship  predict  KNN 
學科別分類
中文摘要 現今企業常會利用資料探勘等技術分析顧客可能的下一步走向與趨勢期望贏得顧客高滿意度的同時亦能增加自身的獲利機會,因此資料的分析能力在今日的大環境中顯得相當重要。但資料的型態並不總是以精確的數值形式出現,對於一些模糊不清或具有人類思維的語言變數便無法以傳統方式加以計算分析,於是Zadeh(1965)學者提出模糊理論以解決此類問題至今已廣泛應用在多個領域上而模糊時間序列預測便是其中之一,由於方法的易用性與高可解釋能力,後續有不少學者致力於該研究領域上。
時間序列在本質上常具有區間震盪的情況發生,意即並非每一次的結果都是剛好落在同一數值上,因此雖然高階模糊邏輯關係能夠補捉到可能的趨勢,但是對於些微的區間變化可能便因為找不到相匹配的模糊邏輯關係而無法準確預測且在模糊時間序列上就算規則相同也不代表語意類別會完全一致,然而現今的文獻中又多以規則完全相同為解模糊之依據。因此本研究在高階模糊時間序列的模糊化與規則建立中不只以隸屬度最大為唯一語意代表,而是分為主要語意類別與次要語意類別並結合KNN的方式找尋相似的模糊邏輯規則。
此外,雖然高階模糊邏輯關係能透過多個時間與時間的遞移關係捕捉到可能趨勢,但有時預測結果較佳的階數卻相當的大而造成實務上的不便。為了有效降低階數的使用但又能保有最佳階數的高準確度,因此本研究在第二階段提出最短鄰近階數(MRO)的概念,透過LHS獨特性與RHS一致性方法找出每筆在訓練資料集中最短可用之階數以提早進行預測。
英文摘要 Businesses usually use data mining or variety of techniques to analyze the likely next step of customer’s behavior or trend in order to get the higher satisfaction and also increase self-profits. Therefore, the capability of data analysis is quite important in today’s situation. However, the data type may come in fuzzy which cannot be solved in traditional mathematics. Until Zadeh(1965) proposed fuzzy theory the questions finally saw the daylight. Fuzzy theory is now used in so many fields and fuzzy time series is one of them.
Time series usually oscillate between likely trends in nature. Although high order fuzzy logical relationship may capture trends in time series, it still cannot precisely predict the situation which cannot find the same FLR in training data. Even if we can find the same FLR, it doesn’t mean that the linguistic class will be the same. Therefore, in the steps of fuzzification and rule establishment we not only use the larger membership degree be a linguistic class, but also take the smaller one into consideration and separate it into major and minor linguistic class. Finally we use KNN method to search the similar FLR.
Although high order FLR may capture the likely trends, sometimes the best order which is quite large will lead to inconvenient in practice. In order to use the shortened order but also keep the high accuracy, we proposed the concept of minimum recent orders (MRO) in second stage. We use the uniqueness of LHS and consistency of RHS to find the MRO in each record in the training data to early predict the result.
論文目次 摘要 III
Abstract IV
誌謝 V
Table of Contents VI
List of Tables VIII
List of Figures X
CHAPTER I Introduction 1
1.1 Background and motivation 1
1.2 The goal and the contribution of this study 4
1.3 The structure of the this study 5
CHAPTER II Literature Review 7
2.1 Fuzzy Theory 7
2.1.1 Fuzzy Set 7
2.1.2 Fuzzy Set Theory 8
2.1.3 Fuzzy Composition Operator 9
2.2 Fuzzy Time Series 10
2.2.1 Basic Definition of Fuzzy Time Series 10
2.2.2 Forecasting Model of Fuzzy Time Series 16
2.3 K Nearest Neighbors Classification Method 30
CHAPTER III Model Development 32
3.1 High-order Fuzzy Time Series Forecasting Model 32
3.2 Minimum Recent Orders (MRO) Establishment 37
3.2.1 Minimum Recent Orders (MRO) 37
3.2.2 Defuzzification Method for Minimum Recent Orders 41
CHAPTER IV Experiment and Evaluation 42
4.1 Evaluation Indicators 42
4.2 The Experiment of Proposed Method 43
4.2.1 Experiment on high-order fuzzy time series forecasting model 43
4.2.2 Experiment on minimum recent orders (MRO) 47
4.2.3 Demonstrate the new defuzzification method for using MRO 50
4.3 Evaluation and Analysis 51
4.3.1 Evaluation of high-order fuzzy time series forecasting model 51
4.3.2 Evaluation of KNN method 53
4.3.3 Evaluation of minimum recent orders (MRO) 55
CHAPTER IV Conclusion and Future Work 56
5.1 Conclusion 56
5.2 Future work 57
Reference 58
參考文獻 Chen, S-M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81(3), 311-319. doi: 10.1016/0165-0114(95)00220-0
Chen, S-M. (2002). Forecasting enrollments based on high-order fuzzy time series. Cybernetics and Systems, 33(1), 1-16. doi: 10.1080/019697202753306479
Chen, S-M, & Chen, C-D. (2011). Handling forecasting problems based on high-order fuzzy logical relationships. Expert Systems with Applications, 38(4), 3857-3864. doi: 10.1016/j.eswa.2010.09.046
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. doi: 10.1016/j.techfore.2005.07.004
Cheng, C-H, Chen, Y-S, & Wu, Y-L. (2009). Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model. Expert Systems with Applications, 36(2, Part 1), 1826-1832. doi: 10.1016/j.eswa.2007.12.041
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. doi:10.1142/S0218488503002478
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, T. H-K. (2006). The application of neural networks to forecast fuzzy time series. Physica A: Statistical Mechanics and its Applications, 363(2), 481-491. doi: 10.1016/S0165-0114(00)00057-9
Huang, K., & Yu, T. H-K. (2006). 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. doi: 10.1109/TSMCB.2005.857093
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. doi: 10.1016/S0165-0114(97)00121-8
Joshi, B-P, & Kumar, S. (2012). Intuitionistic fuzzy sets based method for fuzzy time series forecasting. Cybernetics and Systems, 43(1), 34-47. doi: 10.1080/01969722.2012.637014
Lee, L-W, Wang, L-H, & Chen, S-M. (2007). Temperature prediction and TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms. Expert Systems with Applications, 33(3), 539-550. doi: 10.1016/j.eswa.2006.05.015
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. doi: 10.1109/TFUZZ.2006.876367
Li, S-T, & Cheng, Y-C. (2007). Deterministic fuzzy time series model for forecasting enrollments. Computers & Mathematics with Applications, 53(12), 1904-1920. doi: 10.1016/j.camwa.2006.03.036
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. doi: 10.1016/j.camwa.2008.07.033
Liu, H-T, Wei, N-C, & Yang, C-G. (2009). Improved time-variant fuzzy time series forecast. Fuzzy Optimization and Decision Making, 8(1), 45-65. doi: 10.1007/s10700-009-9051-8
Shah, M. (2012). Fuzzy based trend mapping and forecasting for time series data. Expert Systems with Applications, 39(7), 6351-6358. doi: 10.1016/j.eswa.2011.12.036
Song, Q., & Chissom, B.S. (1993). Forecasting enrollments with fuzzy time series — Part I. Fuzzy Sets and Systems, 54(1), 1-9. doi: 10.1016/0165-0114(93)90355-L
Song, Q., & Chissom, B.S. (1994). Forecasting enrollments with fuzzy time series — part II. Fuzzy Sets and Systems, 62(1), 1-8. doi: 10.1016/0165-0114(94)90067-1
Sullivan, J., & Woodall, W.H. (1994). A comparison of fuzzy forecasting and Markov modeling. Fuzzy Sets and Systems, 64(3), 279-293. doi: 10.1016/0165-0114(94)90152-X
Yu, H-K. (2005). Weighted fuzzy time series models for TAIEX forecasting. Physica A: Statistical Mechanics and its Applications, 349(3–4), 609-624. doi: 10.1016/j.physa.2004.11.006
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. doi: 10.1109/TSMCB.2010.2042055
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. doi: 10.1016/j.eswa.2007.12.013
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2023-12-31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