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..