本文旨在利用台灣加權股價指數TAIEX衍生之技術指標預測未來市場漲跌趨勢，藉由集成學習方法提升整體機器學習預測效果，結合羅吉斯迴歸、隨機森林、支持向量機三個異質演算法，增加模型間之差異性，並依據個別模型的特性，採用不同變數挑選方式，以提升資料品質，最終以單一模型作為標竿模型比較預測成效。整體而言，集成學習後之預測結果較單一模型具有更高的準確度，特別針對預測漲的部分，集成學習的效果較顯著，此外在長天期的趨勢預測中，集成學習的效果也更加明顯。

This study aims to enhance prediction of trends on TAIEX with ensemble learning. As the input, several technical indicators are selected to train the model. To increase diversity of ensemble model, we used three heterogeneous models (logistic regression, random forest, support vector machine) instead of homogeneous models as component learners. Besides, depends on characteristic of component learners, different methods of feature selection are applied to increase the quality of data. To evaluate performance of ensemble models, we used single classifier models as benchmark models, and we found that accuracy of ensemble models is higher than single models. Especially in long-term case, the improvement of ensemble learning is more significant.

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