本論文呈現了一個結合改良型灰預測，小波轉換及支持向量機的預測模型。首先，論文中提出了一個比原始灰預測GM(1,1) 更加優秀的改良式灰預測模型- GGMM(1,1) (Grey number Grey Modification Model)。除此之外，小波轉換被用來當作降低原始時間序列雜訊的前置處理器。經實驗顯示，若將時間序列經由適當的小波轉換處理後作為灰預測的輸入資料，可以有效地提升灰預測的預測精準度。也由於GGMM(1,1) 是一個產生具有區間預測值的灰數預測模型，因此本論文選擇支持向量機來將其灰數區間值轉換成適當的預測值。
本論文使用了台灣加權指數的資料(Taiwan Weighted Stock Price Index, TAIEX) 做為實驗的數據來源。經由實驗產生的結果與其他的論文比較，顯示本論文所提出的預測模型有較佳的效果。

This study presents a method combined with grey prediction, wavelet transform and SVM to improve the accuracy of prediction. Instead of the original grey prediction model GM(1,1), this study shows a new modified GM(1,1) model, which can be used to improve the prediction accuracy, called GGMM(1,1) (Grey number Grey Modification Model). The wavelet transform is used to reduce noise in this study which will improve the prediction significantly. Since GGMM(1,1) produces grey number prediction whose prediction values are between the lower limit and the upper limit, SVM is used to establish the mechanism to select the best predictive value between the lower limit and upper limit of GGMM(1,1).
This study chooses Taiwan Weighted Stock Price Index (TAIEX) as the object of this experiment. It also compares the experimental results with other studies. The final result shows that this study has better prediction effect than the other models.

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