在過往的20年間，灰預測模型(Grey Model)與其衍伸方法已經驗證其對於短期時間序列資料的學習效果。然而，從管理角度視之，單一預測值對於一個未來事件而言，可能是資訊不足的。在處理資料不確定性時，為了更進一步推論灰模型其預測值之發生可能性，本研究開發一個短期時間序列資料的模糊化方法，並基於計畫評核術(Program Evaluation and Review Technique; PERT)，而提出一個新穎的灰預測學習流程。實驗結果顯示，本研究方法在一筆包含八類產品以及一筆公開資料集中，顯示較傳統灰預測模型有更好的預測效果，冀能於實務上協助公司決策者於使用灰模型時，能有更多資訊以利策略之決斷。

Over the past two decades, for dealing with short-term time series data, the grey model (GM) and its extents have been used a lot as effective tools. However, from the management aspect, depending on a single prediction for a future event could lack of enough information. For the purpose of inferring the occurrence probabilities of GM predictions with uncertain data, a novel GM learning procedure based on a series fuzzification method and the PERT (Program Evaluation and Review Technique) is constructed. A real case picked from a giant company in the integrated-circuit assembly industry as well as a public dataset gained from the UC Irvine machine learning repository was examined in the proposed procedure. From the experimental results, more accurate GM predictions are aggregated by the proposed procedure can be found. Besides, the proposed procedure can be used for inferring the occurrence probability of estimators or possible ranges, and managers thus are able to make a better decision by employing the information.
Keywords: Short term time series data; Grey models; Program Evaluation and Review Technique

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