金融時間序列含有高頻(high-frequency)、不穩定性(non-stationary)、定態混沌(deterministically chaotic)及含有眾多雜訊((inherently noise)等性質，單純的只使用原始的股價資料未能提供令人滿意的預測的結果，因此利用灰色關聯分析(grey relational analysis)和離散小波框架轉換(discrete wavelet frame transform)進行特徵萃取，提取出隱含的資訊，提高預測的精準度。而不同的特徵所組出的預測模型表現出來的預測能力也會有所不同，因此在選擇特徵的組合，對於建立預測模型是極為關鍵的，利用基因演算法(genetic algorithms)進行特徵選擇(feature selection)將對於預測沒有幫助的特徵去除，篩選出最佳的特徵組合，最後利用支援向量迴歸 (support vector regression)進行訓練，建立預測模型，來預測台股收盤價。以五年的臺灣加權指數作為實驗對象改善預測精準度，且優於已有的兩個模型 (Chen’s 和Huarng & Yu’s 模型) 。

The financial time series include high-frequency, non-stationary, deterministically chaotic and contains a lot of inherently noise. Simply use only the original stock price data failed to provide satisfactory prediction of performance. Therefore, this paper using Grey Relational Analysis (GRA) and Discrete Wavelet Frame Transform (DWFT) for feature extraction, extract hidden information, to improve forecast accuracy. But different feature set of prediction models for their predictive capacity may vary. Therefore, how to select the feature set is very important. This paper using Genetic Algorithms (GA) to select the better set, last using Support Vector Regression (SVR) for training, the establishment of prediction model to predict Taiwan weighted stock index (TAIEX) for increasing the forecasting accuracy. It is evident that the proposed approach gets the better result performance than that of the other methods.

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