A substantial portion of the network traffic can be attributed to autonomous network applications that experience repeating networking patterns. This observation is further signified by the emergence of the Internet of Things (IoT) era that features an enormous number of networked, autonomous sensors. Identifying and characterizing repeating patterns therefore become a critical means to Internet measurement and traffic engineering. In this paper, we propose a novel method that can effectively identify and characterize timing-based repeating patterns from network traffic by overcoming three significant practical challenges, including i) time-scale sensitive, ii) transience, and iii) being interleaved by noises. Our method features a novel metric, namely unpredictability index (UPI), to capture repeating patterns by quantifying the predictability of packet arrivals’ temporal structure from the perspective of hierarchical clustering. An online approach is further developed to incrementally compute UPI upon observing a single packet. Extensive experiments based on synthetic and real-world data have demonstrated that our method can effectively conduct repeating pattern mining.