時間數列分析過程，常常發現其走勢，隨著時間過程而演變，應用傳統的線性模式來配適，往往很難獲得合適預測值。因此近幾年來，非線性時間數列結構性改變的研究越來越受到重視，也一直是時間數列及計量經濟學學者所熱衷的研究主題之一。本文利用模糊理論的觀念，以模糊炳找出配適ARCH模式數列之轉折區間，分別以轉折區問起始點及結束點作為截取點，去配適ARCH(1)模式，稱之為截取式自迴歸條件變異數分析法(Trimmed ARCH(1) model)。針對台幣對美元銀行間每日收盤匯率，分別以單變量ARIMA、ARCH(1)、Trtmmed ARCH(1)來建構模式，並做比較分析。比較結果發現，以轉折區間結束點作為截取點之Trimmed ARCH(1)模式，其預測值最為準確，大為改善了原來ARCH(1)模式之預測水準。 / In time series analysis, we often find the trend of which changing with time. Using the traditional model fitting can't get a good prediction. Hence the research of structure change of non-linear time series is attentive in recent years, and non-linear time series analysis is a research topic which the scholars of time series and econometrics are intent on. This article tries to use the theory of fuzzy ,to recognize the structure change period by the fuzzy classification, let the first point and the last point of the structure change period be the cute points, to fit ARCH(1) mod ie which we called the Trimmed ARCH(1) model. We use the data of the exchange rate between N.T dol liars and U.S dollars to compare the ARIMAwith ARCH(1) and Trimmed ARCH(1), the forcasting performance shows that Trimmed ARCH(1) model takes a better prediction result.