台灣製造業高度依賴半導體與電子產業，工業生產指數之變動對景氣循環具有重要指標意義，然現有機器學習總體預測文獻甚少針對台灣工業生產指數進行系統性方法比較。本文建立涵蓋 116 個月度總體經濟變數之高維度資料庫（2004 年 3 月至 2025 年 6 月），採固定樣本外預測期數 120 個月之滾動視窗架構，比較傳統時間序列基準模型、正則化線性模型、非線性與集成學習方法、因子模型及混合模型於向前一期至兩年期共五種預測視界下之樣本外表現。實證結果顯示，Component-wise L2-Boosting 與混合模型 Hybrid RF-OLS 於各視界均表現穩健，正則化模型於半年期預測具特殊優勢，隨機森林與 Boosting 等集成方法則於長期預測逐漸勝出，而傳統因子模型表現普遍落後；驅動預測之關鍵變數亦隨視界由貿易與勞動市場訊號，轉向資本市場、利差乃至美國貨幣政策訊號。本文為台灣工業生產指數提供涵蓋多視界、多方法之系統性機器學習預測比較，可作為政策制定與產業分析之參考依據。

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