本論文根據模糊時間序列及模糊邏輯關係提出兩個處理預測問題之新方法。在本論文的第一個方法中，我們根據模糊時間序列來預測台灣股價加權指數，並根據歷史資料建立一階模糊時間序列，對主要因素的變化量(台灣股價加權指數)及第二因素的變化量(道瓊工業指數、那斯達克、台灣貨幣供給額M1b 或兩個以上的第二因素的變化量之結合)做模糊化，來提高預測準確率。在第二個方法中，我們根據高階模糊邏輯關係提出一個新方法來預測台灣股價加權指數、阿拉巴馬大學學生註冊人數及貨物需求量，其能得到更好的預測準確率。

In this thesis, we present two new methods to handle forecasting problems based on fuzzy time series and fuzzy logical relationships. In the first method, we present a new method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy time series. The proposed method constructs the first-order fuzzy logical relationship based on the historical data and fuzzifies the variation of the main factor (TAIEX) and the secondary factor (the Dow Jones, the NASDAQ, the M1b (Taiwan) or their combinations) for forecasting the TAIEX to increase the forecasting accuracy rate. In the second method, we present a new method for the TAIEX prediction, the enrollments of the University of Alabama prediction and the inventory demand prediction based on high-order fuzzy logical relationships. The proposed method constructs high-order fuzzy logical relationships based on the historical data and uses the fixed length of intervals in the universe of discourse for the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) prediction, the enrollments of the University of Alabama prediction and the inventory demand prediction. The proposed methods can get higher forecasting accuracy rates than the existing methods.

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