如何從短期時間序列資料中學習其行為特徵並以為預測模式建構之事前知識，具有實務上應用的價值。然由於資料不足以擷取其行為特徵，因此鄧聚龍教授於1982年提出灰預測模式(grey model, GM)，其藉由累加生成方式，確實提供了一個求解方向，而其中影響該模式預測準確度最重要的因素為背景值的產生方式，尤以參數 值為重，然而如何決定 值實屬於最佳解搜尋過程。此外，而當資料隨時間逐漸發生時，各期資料應視為不同母體分別處理(Lin and Li, 2009) ，因此本研究基於蟻群演算法 (ant colony system, ACS)，提出一個新的自設啟發式模式，針對所取得n期資料之前n-1期資料搜尋各最適 值，並使用盒鬚圖(box-and-whisker plots)對於n期資料分佈進行第n期 值之動態推估，稱之為ACSBWGM(1,1)。在研究資料部分，本論文除蒐集國內某知名的面板產業龍頭之需求資料為研究對象，亦從加州大學的知識發現資料庫所取得之公開測試資料進行效果驗證並與其他多種改良式灰預測模式進行比較。實驗結果顯示，本研究所提出的ACSBWGM(1,1)在預測準確度比較上，確實較其他多種改良式灰預測模式為佳，並能有效增進傳統灰預測模式之預測效能，祈能提供與個案公司對於未來產能規劃有所助益。

It is of practical value to abstract the pattern behavior from short time-series data as the prior knowledge to build forecasting models. However, to achieve this is quite difficult because of the insufficient data size. In 1982, the grey model (GM), proposed by Deng, overcame the issue through the accumulated generating operations (AGO). One important factor that affects the accuracy of the GM model seems to be the background values, but the coefficient that determines those actually plays the key role. How to decide the value can be considered as an optimal solution searching process. In addition, the data should be treated as different populations when a new datum occurs as time increases at each stage (Lin and Li, 2009). Therefore, this research proposes a new heuristic process, developed based on the ant colony system (ACS), to help find the values of the first n-1 series data, and then employs the box-and-whisker plots to examine the distribution to dynamically determine the of the nth datum. The modeling process is thus called the ACSBWGM(1,1) model. The experiment data sets contain that collected from the leading TFT-LCD manufacturer in Taiwan, and that obtained from a public database. The results demonstrate that the ACSBWGM(1,1) model outperforms the traditional and some other modified GM models. Finally, it can be expected to be helpful for the case company when scheduling the future production plans.

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