This paper presents a hybrid approach to automatic incident detection (AID) in transportation systems. It combines time series analysis (TSA) and machine learning (ML) techniques in light of the fault diagnosis theory. In this approach, the time series component is to forecast the normal traffic for the current time point based on prior (normal) traffic. The ML component aims to detect incidents using features of real-time traffic, predicted normal traffic, as well as differences between the two. We validate our approach using a real-world data set collected in previous research. The results show that the hybrid approach is able to detect incidents more accurately [higher detection rate (DR)] and faster (shorter mean time to detect) under the requirement of a similar false alarm rate (FAR), as compared with state-of-the-art algorithms. This paper lends support to further studies on combining TSA with ML to address problems related to intelligent transportation systems (ITS).