本論文提出一種新穎的預測方法GFLW_GRVG(Grey Fourier FCM-LVQ based on GRVG with Wavelet correction)，其以股票價格的漲跌幅度來分析，進而提高未來股價預測系統的準確性。
此預測系統先使用灰色預測搭配傅立葉殘差修正預測未來股票指數，再由LVQ(Learning Vector Quantization)神經網路與FCM(Fuzzy C-means) 網路進行分群分析。群聚分析中FCM-LVQ網路基於GRVG(Grey Relational Vector Grade)進行分析，新的分析方法將與傳統的LVQ神經網路不同，它能更準確的篩選出獲勝神經元。再將股價指數的漲幅度經由類神經網路群聚分類後，訓練出訓練區間資料的分類方法，並且根據此分類方法得到預測的股價指數的群集，經由群聚分析後的群集有自己本身的特性，將這些群集分別以小波轉換修正其誤差值，以獲得最佳的預測值。
經實驗結果證明，GFLW_GRVG預測系統改善了參考值不足及運算時間的問題，讓此預測系統不論是趨勢走向或是盤整走向都有顯著的進步，更擁有高準確度的預測表現。

This thesis proposes a novel forecasting method, GFLW_GRVG, using the growth rate of stock prices to analyze and raise the accuracy of stock price prediction systems in the future. The prediction system involves grey prediction with Fourier residual correction to forecast the future stock index, which will be analyzed by LVQ (learning vector quantization) neural network and FCM (fuzzy C-means) network. In cluster analysis, the FCM-LVQ network analysis is based on GRVG (grey relational vector grade). The new analytical method which can select winning neurons more precisely will be different from the traditional LVQ neural network. The artificial neural networks create clusters out of the growth rate of the stock index and train the new analytical method to classify interval data, predicting the clusters of stock index via this classification method. The error rate of the analyzed clusters with their own properties will be corrected by wavelet transform so that the best prediction value can be found.
Experimental results show that the GFLW_GRVG prediction system improves the problems of insufficient reference value and operation time. The forecasting system has significant progress not only in stock trends but also in stock consolidation. Furthermore, its predictive performance obtains high accuracy as compared to other schemes.

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