短期時間序列資料的預測於實務應用方面有其重要意義，然而卻因資料過少而有其學習困難性，如何增進其預測準確度是一項重大的挑戰。灰預測模式於1983年由鄧聚龍教授提出後，有其顯著的發展並有許多實務應用，然由於其對於背景值的計算並未考量資料發生趨勢，因此Li et al. (2009) 藉由趨勢潛力追蹤法(Li and Yeh, 2008)來修訂其各階段計算背景值所使用之參數 而提出名為適應性灰預測模型，其結果顯示較傳統以及其他多種改良式灰預測模式有更佳的預測效果。事實上，求解各期 值是屬於最佳化搜尋的過程，因此本研究使用基因演算法針對各期 進行最適求解，以建構更具準確性的適應性灰預測模型。在研究資料方面，本論文以國內某知名的面板產業龍頭為研究對象，並以從公開測試資料進行效果驗證，結果顯示本研究方法確有更佳的預測準確度。

The prediction of short-term time series data is of value in practice, but it is difficult to be learned because of the limited data size. Therefore, it is a great challenge to improve the preciseness of predictions when dealing with short-term time series data. The Grey Model (Deng, 1983), developed to overcome the problem, has significant developments in theories and applications in real world in decades. However, the model does not take the occurring trend of data into consideration when calculating the background values, and could result in imprecise predictions. Li et al. (2009) proposed a new model named the Adaptive Grey Model (AGM), developed based on the trend and potency tracking method (TPTM), to generate the parameter when a new datum is occurring, and the experimental results demonstrated AGM has better preciseness than the other improved GM models. In fact, generating the in each term is the process in searching the optimal solution. This paper thus applies the genetic algorithm (GA) to achieve this based on the generated by AGM to build a more accurate model, called GAAGM(1,1). To verify the effectiveness of the GAAGM(1,1) model, this study employs two kinds of data, the Synthetic Control Chart Time Series dataset (SCCTS) from the Knowledge Discovery Database (KDD), and the manufacturing data collected from a leading manufacturer of thin film transistor liquid crystal display (TFT-LCD) panels in Taiwan, to construct a grey model that is compared with the other such models, AGM(1,1), IGM1(1,1), IGM2(1,1), and NGBM(1,1). The experimental results show that GAAGM(1,1) can achieve significantly more precise.

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