近幾年來，有許多學者專家提出以模糊時間序列來處理預測問題的方法。很明顯的，一個事件可能會被多項因素所影響。因此，在處理預測問題時，考慮多項因素將會比只考慮一項因素作出更準確的預測。本論文根據遺傳演算法、模擬退火演算法及高階模糊時間序列提出三個處理預測問題之新方法。在本論文的第一個方法中，我們根據兩因子高階模糊時間序列提出一個新方法以做溫度預測及期貨指數預測，並根據歷史資料以建立兩因子高階模糊邏輯關係，以提高預測準確率。在第二個方法中，我們根據遺傳演算法及兩因子高階模糊時間序列提出一個新方法以作溫度預測及期貨指數預測，並利用遺傳演算法來調整論述宇集中各區間之大小來作預測，以提高預測準確率。在第三個方法中，我們將第二個方法作改進，將模擬退火法來處理遺傳演算法中的突變運算，以有效避免落入區域最佳解，以提高預測準確率。本論文所提之方法比目前已存在的方法具有更高的預測準確率。

In recent years, many researchers used fuzzy time series to handle prediction problems. It is obvious that an event may be affected by many factors. For dealing with forecasting problems, if we consider more factors for prediction, then we can get better forecasting results. In this thesis, we present three new methods for dealing with forecasting problems, based on genetic algorithms, simulated annealing algorithms and high-order fuzzy time series. In the first method, we present a new method to predict the temperature and the TAIFEX (Taiwan Futures Exchange), based on the two-factors high-order fuzzy time series. The proposed method constructs two-factors high-order fuzzy logical relationships based on the historical data to increase the forecasting accuracy rate. In the second method, we present a new method for temperature prediction and the TAIFEX forecasting based on genetic algorithms and two-factors high-order fuzzy time series. The proposed method constructs two-factors high-order fuzzy logical relationship based on the historical data and uses genetic algorithms to adjust the length of each interval in the universe of discourse for temperature prediction and the TAIFEX forecasting to increase the forecasting accuracy rate. In the third method, we improve the second method to present a new method for temperature prediction and the TAIFEX forecasting based on genetic simulated annealing techniques and high-order fuzzy time series, where the simulated annealing techniques are used to deal with the mutation operations of genetic algorithms and can avoid falling into the local optimum effectively for increasing the forecasting accuracy rate. The proposed methods get higher forecasting accuracy rates than the existing methods.

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