由於人類的思維具有模糊性和不確定性的特性，傳統的時間序列已經無法滿足所需。模糊時間序列由Song及Chissom (1993) 利用Zadeh (1965) 提出的模糊集合理論發展，除了廣泛的應用在預測方法外，儼然已成為決策問題的解決之道。而後有學者直接或間接證明，其處理模糊及不確定性的資料具有相當大的可信度以及貢獻。然而，在眾多的模糊時間序列研究中，大多數方法採用相同樣本(in-sample)進行訓練和預測，較少以不同樣本(out-sample)的研究，雖然不同樣本的預測模式較貼近實際預測的情境，但是在歷史資料上找不到預測法則的情況，卻是讓許多學者卻步的原因之ㄧ。
在模糊時間序列預測中又可分為兩個議題：其一是足以代表模糊時間序列模型的關鍵因素－模糊邏輯關係；另一個是常應用於關聯矩陣的模糊關係式。模糊邏輯關係是以挖掘歷史資料之間的隱藏關係為主，而模糊關聯矩陣則具有紀錄歷史資料模糊歸屬度轉換能力。因此，本研究提出一個改善的決定性預測模型，整合模糊邏輯關係與模糊關聯矩陣之優點，用以充分解決歷史資料無法匹配的議題，期能處理更精確的模糊時間序列之預測結果。
為驗證本研究所提出的預測模式，本研究應用來自於世界權威金融分系機構的美國標準普爾公司(Standard & Poor，S&P)、台灣中央氣象局(Taiwan Central Weather Bureau)以及雅虎財經(Yahoo! Finance)之真實資料庫，並將預測結果與其他模糊時間序列的模型做比較。為了衡量預測模式的綜合成效，以預測精準度、語意精準度、趨勢準確度三種評估指標比較實驗結果，期本研究之預測模式能協助決策者更精確地進行決策之參考。

Due to human-thinking pays lots of attention dealing with vagueness and incompleteness inherent in data, traditional time series approaches couldn’t satisfy the forecasting problem. Fuzzy time series was first proposed by Song and Chissom (Song & Chissom, 1993a), and it has been widely applied not only on forecasting method but also the solution for decision making. The reliability of handling ambiguous data and contribution of the framework of Song and Chissom’s model were proofed directly and indirectly by more and more researchers.
However, Fuzzy time series forecasting can be divided into two issues: One is fuzzy logical relation - a crucial connector in presenting fuzzy time series model, and another is fuzzy relational equations, employed based on relation matrix. Fuzzy logical relation used to discover hidden relations between historical data, and fuzzy relation matrix has the ability of recording membership degree’s transformation. In this study, we proposed an improved deterministic forecasting model, and integrated the advantages of fuzzy logical relation and fuzzy relation matrix in order to deal with nonmatching problems and achieve the better accuracy.
Finally, we apply the data sets which extract from Standard & Poor (S&P), Taiwan Central Weather Bureau and Yahoo Finance in our proposed model, moreover, we compare to the other fuzzy time series forecasting models. To verify the performance and the effectiveness of the forecasting model, we evaluated the result of experiments using three kinds of indicators: prediction accuracy, linguistic accuracy, trend accuracy.

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