近幾年來，模糊時間序列已被用在處理各種預測問題上。藉由觀察變化的趨勢，運用模糊時間序列可以得到更好的預測結果。一般而言，一個預測的問題可能不只有一項重要因素，如果我們能考慮多個因素並能適切地運用它們，我們將能得到更高的預測準確率。
本論文根據模糊時間序列及自動產生之多因子解模糊化之預測模糊變化量的權重以預測台灣加權股價指數。首先我們利用鄰近交易日歷史資料的漲跌幅來分別產生主要因素(即台灣加權股價指數)、第二因素(即道瓊工業指數、那斯達克指數及台灣貨幣供給額M1B)及台灣加權股價指數鄰近交易日間的模糊變化關係群組。由某個交易日的主要因素和第二因素漲跌幅，我們可以分別預測下一個交易日台灣加權股價指數可能的漲跌幅。然後再藉由主要因素所預測的台灣加權股價指數漲跌幅及第二因素所預測的台灣加權股價指數漲跌幅之間的相關係數之大小來判斷是否要同時使用主要因素和第二因素來做預測，並以此相關係數做為計算主要因素預測結果和第二因素預測結果的權重之依據。最後利用該交易日的加權指數，乘上主要因素和第二因素加權後的預測漲跌幅，我們可以得到下一個交易日的加權指數預測值。實驗結果證明本論文所提的方法比目前已存在的方法具有更高的預測準確率。

In this paper, we present a new method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy time series and the automatic generated weights of defuzzified forecasted fuzzy variations of multiple-factors. First, our method uses the variation magnitude of adjacent historical data to generate fuzzy variation groups of the main factor (i.e., the TAIEX) and the elementary secondary factors (i.e., the Dow Jones, the NASDAQ and the M1B), respectively. Then, based on the variation magnitudes of the main factor TAIEX and the elementary secondary factors of a particular trading day, it gets the forecasted variation of the TAIEX of the next trading day forecasted by each factor. Then, by calculating the correlation coefficient between the numerical data series of the main factor (constructed by the forecasted fuzzy variation of the main factor) and the numerical data series of each elementary secondary factor (constructed by the forecasted fuzzy variation of each elementary secondary factor), respectively, it determines the relevance between the forecasted variation of the main factor and the forecasted variation of each elementary secondary factor. If the correlation coefficient between the numerical data series of the main factor and the numerical data series of an elementary secondary factor is smaller than or equal to zero, then it does not adopt the elementary secondary factor to avoid the reduction of the accuracy of the forecasting of the main factor. Otherwise, if the correlation coefficient between the numerical data series of the main factor and the numerical data series of an elementary secondary factor is larger than zero, then it adopts the elementary secondary factor to help forecasting the main factor. Then, based on the correlation coefficients between the forecasted fuzzy variation of the main factor and the forecasted fuzzy variation of each elementary secondary factor, it automatically generates the weights of the defuzzified forecasted fuzzy variation of the main factor and the defuzzified forecasted fuzzy variation of each elementary secondary factor, respectively, to enhance the forecasting accuracy. Finally, based on the closing index of the TAIEX of the trading day and the weighted forecasted variation, it generates the final forecasted value of the next trading day. The experimental results show that the proposed method outperforms the existing methods.

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