數據科學的蓬勃發展使得人們較不需要特定領域的專業知識，便能進行預測與分析。此乃基於大數據的分析可以從海量的數據中發現相關性、以及市場趨勢與客戶偏好，協助組織做出明智的決策。隨著全球競爭激烈，產品生命週期越來越短，快速回應市場需求是取得市場先機的重要因素。然在產品開發初期階段，卻很難收集到足夠的數據進行有效分析。針對上述問題，本研究提出了一種結合灰色生成增加樣本數據和模糊迴歸進行驗證及預測的漸進式系統。為了在時間上充分利用數據，生成的樣本數據將進行相應的校準，此設計不僅提高預測精度，同時也修正一般模糊迴歸模型的區間愈加發散的現象，進而驗證數據愈多，預測愈精準的統計法則，而使本研究的系統的更新區間逐漸收斂至統計迴歸線。基於資料取得與文獻比較的可及性，本研究以鋰離子電池容量衰減預測為例進行說明和評估。

The rapid development of data science has made it possible to make predictions and analyses without the need for specialized knowledge in a specific field. Based on Big Data Analytics, the result can discover correlations, market trends, and customer preferences from large amounts of data to help organizations make decisions. However, due to the fierce global competition and short product life cycle, it is hard to collect sufficient data for performing effective analysis, especially in the early stage of development. To solve the above problems, this study proposes a progressive system that combines Grey Generation to increase sample data and Fuzzy Regression for validation and prediction. To make the best use of data along time, the generated sample data will be calibrated accordingly to increase the prediction accuracy. Based on such design, the proposed system not only overcomes the shortcoming of diverse intervals derived from general fuzzy regression models, but also, from the convergent trend of the updated data, demonstrates that the more data, the more accurate the system becomes, which is consistent with the statistic law. Due to the accessibility and comparability of the data and the related research, a case of the capacity decay prediction of Lithium-Ion Battery is employed for illustration and evaluation.

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