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。

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.

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