傳統的模式辨別系統係使用單一演算法進行資料的學習，但此並無法滿足不同行為模式者，故於過去數十年間機械學習法之開發有蓬勃之發展。近十年來，所開發之單一學習法已屆瓶頸卻仍未見可滿足不同行為特性之資料者，因此許多學者嘗試結合多種學習法以求進階之效果改善，稱之為多模整合法。然此些整合法之研究主要琢磨於模式整合過程，對於如何整合其結果之研究相對甚少，如分類預測問題藉由投票多數決方式整合，但數值預測問題方面，則大多如Yu (2011)採簡單平均法為之。因此本研究亦基於整體趨勢擴展(mega-trend-diffusion, MTD)技術提出一個小樣本資料學習下，具合理性之預測數值產生流程，求算各模式於追求最小誤差目標下之預測值，以取得多模系統之穩健性，其中本研究所整合者包含線性迴歸、倒傳遞類神經網路、支援向量迴歸以及M5’模式樹四種預測模式。在方法的效果驗證上，本研究將與簡單平均法以及各模式的預測值進行比較，同時以三個個案進行驗證。實驗結果發現，本研究所提出的方法確實能降低預測誤差。

Single learning algorithm has been developed in the past few decades, but it can not satisfied different kinds of data recently. There are many researches try to combine a variety of learning algorithms to improve learning accuracy called multi-model ensemble method, however these researches focus on its process instead of results. This paper presents an approach that is based on mega-trend-diffusion (MTD) and rational method of numerical prediction to minimize calculation error by compromising solution of models. The used methods include multiple regression, back-propagation network, support vector regression, and M5' model tree. The proposed approach would compare with the simple average method and these models, while three cases study are used to illustrate the details of this research. The empirical results show that the proposed method can reduce models mean absolute percentage error.

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