企業投注新產品開發之成本高昂，使用下游廠商或終端使用者的需求預測做為決斷參考是一可行之策。然此種短期時間序列資料有其學習困難性，在過往的方法中，分數階灰預測模式(fractional grey model, FGM) 已被證實其透過分數累加方式，較傳統的整數累加方式的GM模型有更佳的準確率，然而如何決定適度的分數階值，許多文獻以不同最佳化演算法進行；除分數階值外，如何設定適當的分數階背景值，以進一步地提高其預測準確率，亦值得探究。本研究使用盒形圖，推估資料的發生趨勢，並將此與FGM結合，稱為盒形圖為基礎的分數階灰預測模型(box-plot based FGM, BFGM)。實驗部分，以某知名的設備商為研究對象，透過其生產之商品屬性與公開測試資料來進行效果驗證，經實驗結果顯示，BFGM比FGM有更佳的預測結果。

The cost of investing in new product development is high, and it is a feasible way to use demand forecasts from customer or end-users as a decisive reference. However, this short-term time series data has its learning difficulties. In the past, the fractional grey prediction model (fractional grey model, FGM) has been proved that its cumulative method is better than the traditional integer cumulative of grey model (GM) model. There are many researches using different optimal algorithms to determine the moderate score order. And how to set the coefficient sets of α in grey model is also worth exploring. Therefore, this research reveals a new grey model which used box plot to estimate the trend of data and combined this with FGM, known as the box-plot-based fractional scale prediction model (box-plot-based FGM, BFGM) to improve the accuracy of predictors by setting the coefficient sets of α in traditional grey model. In the experimental, the examined dataset that collected from a well-known equipment manufacturer as the research object. The result verified the effect through the commodity attributes and public test data of its production, and the experimental results show that BFGM has better prediction results than FGM.

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