近年來由於資訊技術的快速成長，數位資料的儲存量越來越多，不論是產生的數位化文件或將傳統文件資料數位化，皆有可能發生小部分的資料因儲存不當而遺失，或因為年代久遠而不易數位化，最後導致資料的不完整或模糊不清的狀況，此時只能依靠人類的經驗填入適當的值作為紀錄。但人們思考的方式往往不是絕對的，而是可能有模糊地帶的思維。以天氣的資料來說，失去上周一的數據資料時，則會回想當時的情境，接著給一個詞語做為代表資料，如：「上星期一很熱」，將此句話以數學理論處理，其實就是模糊理論的基礎背景。Zadeh (1965)學者提出了模糊理論，並提供完整的數學架構做為依據，使模糊理論被廣泛使用。
模糊理論已被廣泛使用於多個領域，其中時間序列與模糊理論的結合，開啟了學者對於模糊時間序列預測模式的研究熱潮，並被證實具有預測不確定性資料的能力。而在眾多法則式模糊時間序列預測模式中，有兩項缺點：(1)高階的模糊時間序列並不能做到準確預測，階數到達某一程度，越高反而準確率越低落。(2)建立的法則使用率過低，建立法則後，在預測階段能夠幫助預測的法則非常少。因此為改善模糊時間序列預測的品質與能力，本研究提出一個新的方法應用於決定性模糊時間序列，採用模糊推論系統取代傳統的預測步驟，使建立的所有模糊邏輯關係法則能提供更多資訊，並改善法則建立的方式，在決定性回溯演算法中加入以統計方法或模糊相似度做為衡量建立新法則的標準，減少不必要的法則。上述提出的方法能夠有效的改善決定性模糊時間序列的預測結果。

Recently, the fuzzy theory, which proposed by Zadeh in 1965, has been widely applied to time series since its capability of handling linguistic terms used in our daily life. The fuzzy time series framework proposed by Song and Chissom solved the missing historical data and linguistic data processing problem in traditional time series.
Our research is focus on rule based fuzzy time series, and we observed two problems. The one is that if there is only one consequent, the number of rules will be more. Secondly, we also noticed that when doing forecasting, the more rules were built, the rule matching conditions were worse. Li and Cheng (2009) pointed that the rule redundancy problem and the high-order redundancy problem are two crucial issues existing in fuzzy time series research.
According to aforesaid two problems, this study proposed a framework based on deterministic fuzzy time series model to reduce rule redundancy problem. We add information similarity method as an indicator to control the number of generated rules when constructing fuzzy relationship. The proposed framework could enhance the utility of rules and forecast accuracy.

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