由於日常生活中充斥著不確定的資料，不確定的性質通常來自於隨機現象與多重隸屬現象，因此，除了傳統時間序列之外，模糊時間序列模式亦日趨重要。然而，目前模糊時間序列的研究仍然存在某些議題：(1)目前的預測模式一是運用模糊關係矩陣進行預測，二則是運用模糊邏輯關係的法則式推論，二者皆並未加入學習的機制，所以比較難以從歷史資料中萃取充分資訊進行預測；(2)在二因子及多因子模糊時間序列模式的相關研究中，並未探討主要因子與次要因子之間的關係，難以呈現各個因子之間的因果關聯性。因此，本研究將提出一個演化式模糊預測模式，將模糊關係矩陣的求算方式改善，提出學習架構使得模糊關係矩陣能夠經過學習以更適性於歷史資料，達到更全面完整性的預測效果；另一方面，將探討二因子模糊時間序列模式中，各因子之間的因果關係，接著圖形化其關聯性，清楚因子之間的關係與影響程度，以視覺化的圖形效果呈現。最後，為了評估本研究所提出的預測模式，將使用三種評估指標，包括預測準確性、語意準確性及趨勢準確性，與其他預測模型做為比較，說明本研究所提出的模式將得到較佳預測效果。

Due to daily life is full of the uncertainty information, the uncertainty nature normally comes from the random phenomenon and the phenomenon of multiple membership. Therefore, in addition to traditional time series, the fuzzy time series forecasting model has become more important. However, there are still some issues in the study of fuzzy time series. (1) Recent research for forecasting model is using fuzzy relational equations and fuzzy logical relation, but both of them did not join the learning mechanisms. This is difficult to extract sufficient information from the historical data to predict. (2) The research of the two-factors and multi-factors fuzzy time series forecasting model did not explore the relationship between the main factor and the secondary factor. It is difficult to show the causal relationship.Therefore, this study will propose an evolutionary fuzzy forecasting model. We improve the calculation of the fuzzy relation matrix, and we propose the learning architecture for fuzzy relation matrix to fit the historical data. On the other hand, we explore the causal relationship between factors in the two-factors fuzzy time series forecasting model, then graph the relationship. Finally, in order to verify the performance and the effectiveness of the forecasting model, we use three evaluation indicators to compare with other forecasting models, including prediction accuracy, linguistic accuracy and trend accuracy.

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., & Chung, N. Y. (2006). Forecasting enrollments using high-order fuzzy time series and genetic algorithms. International Journal of Intelligent Systems, 21(5), 485-501. doi: Doi 10.1002/Int.20145
Chen, S. M., & Hsu, C. C. (2004). A new method to forecast enrollments using fuzzy time series. International Journal of Applied Science and Engineering, 2(3), 234-244.
Chen, S. M., & Hwang, J. R. (2000). Temperature prediction using fuzzy time series. Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics, 30(2), 263-275.
Cheng, C. H., Huang, S. F., & Teoh, H. J. (2011). Predicting daily ozone concentration maxima using fuzzy time series based on a two-stage linguistic partition method. Computers & Mathematics with Applications, 62(4), 2016-2028. doi: DOI 10.1016/j.camwa.2011.06.044
Herrera, F., Lozano, M., & Verdegay, J. L. (1997). Fuzzy connectives based crossover operators to model genetic algorithms population diversity. Fuzzy Sets and Systems, 92(1), 21-30. doi: 10.1.1.50.4447
Herrera, F., Lozano, M., & Verdegay, J. L. (1998). Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis. Artificial Intelligence Review, 12(4), 265-319.
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). Heuristic models of fuzzy time series for forecasting. Fuzzy Sets and Systems, 123(3), 369-386.
Huarng, K. H., Yu, T. H. K., & Hsu, Y. W. (2007). A multivariate heuristic model for fuzzy time-series forecasting. Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics, 37(4), 836-846. doi: Doi 10.1109/Tsmcb.2006.890303
Lee, L.-W., Wang, L.-H., & Chen, S.-M. (2008). Temperature prediction and TAIFEX forecasting based on high-order fuzzy logical relationships and genetic simulated annealing techniques. Expert Systems with Applications, 34(1), 328-336. doi: 10.1016/j.eswa.2006.09.007
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. Ieee Transactions on Fuzzy Systems, 14(3), 468-477. doi: Doi 10.1109/Tfuzz.2006.876367
Lee, Y. C., Hwang, C. Y., & Shih, Y. P. (1994). A Combined Approach to Fuzzy Model Identification. Ieee Transactions on Systems Man and Cybernetics, 24(5), 736-744.
Li, S. T., & Cheng, Y. C. (2007). Deterministic fuzzy time series model for forecasting enrollments. Computers & Mathematics with Applications, 53(12), 1904-1920. doi: DOI 10.1016/j.camwa.2006.03.036
Li, S. T., & Cheng, Y. C. (2009). An Enhanced Deterministic Fuzzy Time Series Forecasting Model. Cybernetics and Systems, 40(3), 211-235. doi: Pii 909179607Doi 10.1080/01969720802715128
Li, S. T., & Cheng, Y. C. (2010). A stochastic HMM-based forecasting model for fuzzy time series. [Research Support, Non-U.S. Gov't]. IEEE Trans Syst Man Cybern B Cybern, 40(5), 1255-1266. doi: 10.1109/TSMCB.2009.2036860
Li, S. T., Kuo, S. C., Cheng, Y. C., & Chen, C. C. (2010). Deterministic vector long-term forecasting for fuzzy time series. Fuzzy Sets and Systems, 161(13), 1852-1870. doi: DOI 10.1016/j.fss.2009.10.028
Li, S. T., Kuo, S. C., Cheng, Y. C., & Chen, C. C. (2011). A vector forecasting model for fuzzy time series. Applied Soft Computing, 11(3), 3125-3134. doi: DOI 10.1016/j.asoc.2010.12.015
Li, Y. (2005). Hidden Markov models with states depending on observations. Pattern Recognition Letters, 26(7), 977-984. doi: 10.1016/j.patrec.2004.09.050
Mühlenbein, H., & Schlierkamp-Voosen, D. (1993). Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization. Evol Comput, 1(1), 25-49. doi: 10.1162/evco.1993.1.1.25
Own, C.-M., & Yu, P.-T. (2005). Forecasting Fuzzy Time Series on a Heuristic High-Order Model. Cybernetics and Systems, 36(7), 705-717. doi: 10.1080/01969720591008922
Park, J. I., Lee, D. J., Song, C. K., & Chun, M. G. (2010). TAIFEX and KOSPI 200 forecasting based on two-factors high-order fuzzy time series and particle swarm optimization. Expert Systems with Applications, 37(2), 959-967. doi: DOI 10.1016/j.eswa.2009.05.081
Song, Q., & Chissom, B. S. (1993a). Fuzzy Time-Series and Its Models. Fuzzy Sets and Systems, 54(3), 269-277. doi: 10.1016/0165-0114(93)90372-O
Song, Q., & Chissom, B. S. (1993b). Forecasting Enrollments with Fuzzy Time-Series .1. 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 .2. Fuzzy Sets and Systems, 62(1), 1-8.
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
Tsaur, R. C., Yang, J. C. O., & Wang, H. F. (2005). Fuzzy relation analysis in fuzzy time series model. Computers & Mathematics with Applications, 49(4), 539-548. doi: DOI 10.1016/j.camwa.2004.07.014
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), 2143-2154. doi: DOI 10.1016/j.eswa.2007.12.013
Zadeh, L. A. (1965). Fuzzy Sets. Information and Control, 8(3), 338-353.