在本論文中，我們根據兩因子高階模糊趨勢邏輯關係群及模糊邏輯關係之 趨勢機率提出一個作模糊預測的新方法。首先，我們分別將主要因子及第二因子的歷史訓練資訊轉換為模糊集合，並且形成兩因子高階模糊邏輯關係。然後，我們將獲得的兩因子高階模糊邏輯關係依照兩因子高階模糊邏輯關係的趨勢分組成兩因子高階模糊趨勢邏輯關係群。然後，我們在每一個兩因子高階模糊趨勢邏輯關係群中計算其中之兩因子高階模糊趨勢邏輯關係之“下降趨勢”的機率、“相等趨勢” 的機率、及“上升趨勢”的機率。最後，我們利用計算出來的“下降趨勢” 的機率、“相等趨勢” 的機率、及“上升趨勢”的機率進行預測。我們亦應用我們所提的方法用來預測台灣股價加權指數及新台幣/美元匯率。實驗結果顯示我們所提的方法優於目前已存在的方法。

In this thesis, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy-trend logical relationships. Firstly, the proposed method fuzzifies the historical training data of the main factor and the secondary factor into fuzzy sets, respectively, to form two-factors second-order fuzzy logical relationships. Then, it groups the obtained two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, it calculates the probability of the “down-trend”, the probability of the “equal-trend” and the probability of the “up-trend” of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group, respectively. Finally, it performs the forecasting based on the probabilities of the “down-trend”, the “equal-trend” and the “up-trend” of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the NTD/USD exchange rates. The experimental results show that the proposed method outperforms the existing methods.

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