血液是人體不可或缺的組織，其供需管理也是當代醫療體系中的重要研究議題。由於能夠完全取代人體血液的替代品尚待開發，目前血液的供給仍須仰賴捐血者，然捐血者的行為常受時間、天氣、活動等因素影響而使血液的供給產生波動；而血液的需求也可能因時間、天氣或重大災害等因素影響而出現不規則且隨機的變化，加上血液本身又是易腐性產品（perishable goods），若庫存管理不當可能會產生血液短缺或造成過期而浪費的現象，因此如何精準預測血品供需在血液庫存管理的研究中長期受到重視。本研究的目的在建構能夠精準預測捐血量之模型，研究中將整合血液基金會提供的捐血資料以及中央氣象局提供的天氣資訊，透過廣義可加性模型（generalized additive model, GAM）及時間序列模型建立兩階段預測模型。接著，本研究透過重抽樣法（bootstrapping）處理前面二階段模型的殘差並據以建構預測區間進行異常偵測。透過此預測模型結合起來的資訊，本研究之結果將可用於預測及偵測血液供應短缺或是剩餘的情況，提供未來血液管理上可行之決策依據。

Blood is a vitally important fluid of the human body, and the management of its supply and demand is an important research issue in contemporary health care systems. Because blood substitute has not yet been well developed, most of the healthcare systems rely on voluntary donations to ensure sufficient blood supplies. However, blood demand and supply can be highly stochastic and irregular because the donation pattern and transfusion need may be affected by various factors including time, weather, and other events (such as disasters). Blood products are also perishable, and an inappropriate inventory management can result in shortage or wastage of blood products. Thus, a model which is capable of accurately predicting blood supply and demand has long been valued in the blood inventory management research. In this study, a forecasting model which can precisely predict the amount of blood donation was developed. In particular, we integrated the data of donation histories of whole-blood donors collected from Taipei Blood Center and the weather information form the Central Weather Bureau, and combined the generalized additive model (GAM) with the time series model to construct a two-step prediction model. A bootstrap method was also implemented on the residuals produced by the two-step model to construct the prediction intervals which can later be used to detect the anomaly donation patterns. The results of this research could have considerable implications in blood inventory management to help achieving better allocation of resources for Blood Centers.

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