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題名：利用灰色系統理論與模糊時間序列預測綠色電子材料成長趨勢
作者：蔡尚斌
作者(外文)：Sang-Bing Tsai
校院名稱：中華大學
系所名稱：科技管理博士學位學程
指導教授：李友錚
學位類別：博士
出版日期：2014
主題關鍵詞：預測；灰色系統理論；GM(1；1) Alpha；啟發式模糊時間序列；小樣本資料；forecasting；grey system theory；GM(1；1) Alpha；Heuristic Fuzzy time series；small data set
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綠色材料的基本理念就是對環境產生最少污染的材料，低污染材料的設計與開發，低污染製程的創新及改善，材料的回收與再利用。印刷電路板(Printed Circuit Board)核心材料是銅箔基板(Copper Clad Laminate)，銅箔基板為了達到環保的目的，利用添加非鹵素型難燃劑於材料上，使產品達到阻燃且具環保之技術。隨著環保法令的訂定，PCB基板材料朝向環保無鹵素(halogen-free)方向的設計開發已是不可抗拒的趨勢。
預測可以估計未來企業所無法確認的事件或情況，並提供管理者做為規劃的基礎，因此預測是決策過程中的重要項目。如何預測綠色銅箔基板材料的成長趨勢，對於印刷電路板業者與銅箔基板業者來說非常重要。業者越先掌握成長趨勢，便可以獲得市場與技術發展的先機。
不過，由於它的歷史資料是屬於小樣本而且資料分佈不屬於常態分配，若使用傳統的迴歸分析方法或時間序列方法來預測，對於前題假設就不合適。為了解決此問題，本研究利用灰色系統理論與模糊時間序列這兩個研究方法，改良三種不同的計算模式(GM(1,1) Alpha、啟發式模糊時間序列(8區間)、啟發式模糊時間序列(16區間))，進行理論的推導與案例的證明，並與迴歸分析方法做比較，確認這幾種研究方法預測的準確性與適合度。從本文案例來看，GM(1,1) Alpha模型與啟發式模糊時間序列(Heuristic Fuzzy time series)模型兩個方法對於小樣本，而且不屬於常態分配的資料，都有良好的預測效果。
The use of green materials reflects the concept that production materials should generate minimal environmental pollution through efforts that include the design and development of low-pollution materials, innovation and improvement of low-pollution manufacturing processes, and recycling and reuse of materials. Copper clad laminate (CCL) is a core material in PCB production. To satisfy environmental requirements, CCL substrates are typically coated with nonhalogen-based flame retardants to reduce flammability, thereby achieving environmental protection. Because of relevant environmental laws and regulations, the development of halogen-free PCB substrate material is an inevitable trend.
In corporate management, forecasting is used to predict unforeseen events and scenarios, and provide managers with a basis for planning; therefore, it has a crucial function in business decision making. Forecasting the growth trend of green copper clad laminate (CCL) material is crucial for manufacturers of printed circuit boards and green CCLs. Early and accurate understanding of such trends in this industry can lead to the early acquisition of opportunities for related markets and technological development.
Because historical data samples associated with green CCL are small and typically lack a normal distribution, employing conventional regression analysis or time series models for forecasting is not suitable for testing related presumptions. To address this issue, this study adopted the Grey Model (GM) and fuzzy time series (FTS) model, and modified three computing models (i.e., GM (1,1) Alpha, heuristic FTS with 8 equal intervals, and heuristic FTS with 16 equal intervals) for theoretical derivation and scientific verification. The results show that effective forecasting can be achieved by applying either a GM (1,1) alpha model or heuristic FTS model to the data of a small and non-normally distributed sample.
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