傳統的序列樣式探勘方法主要從單點型事件序列中找尋頻繁樣式。然而，鮮少序列樣式探勘方法被發展用於同時處理單點型及區間型事件混合的混合時間樣式。本文中，我們將介紹一個新的混合時間樣式探勘演算法，並運用虛擬資料及真實資料進行實驗，驗證所提出之演算法的有效性。實驗結果顯示，在利用混合時間樣式下，其預測能力優於使用傳統單點型及區間型事件序列樣式。

Traditional sequential pattern mining methods are utilized to discover frequent patterns from point-based event sequences. However, few are developed for discovering frequent patterns from sequences consisting of both point and interval-based events, which are called hybrid event sequences. In this paper, we introduce a new hybrid temporal pattern mining algorithm and then carry out an experiment using both synthetic and real data to compare our proposed algorithm with traditional ones designed exclusively for mining point-based patterns or interval-based patterns. The experimental results indicate that the efficiency of algorithm is satisfactory and the predicting power of hybrid temporal patterns is higher than that of point-based or interval-based patterns..