Time series anomaly detection using recessive subsequence

Time series arise frequently in many sciences and engineering application, including finance, digital audio, motion capture, network security, and transportation. In this work, we propose a technique for discovering anomalies in time series that takes advantages of the Symbolic Aggregate approXimation (SAX) technique and inspiration from a motif discovery algorithm. We use SAX to reduce the dimension of the time series and apply the idea of motif discovery to detect anomalies. We consider recessive sequences instead of frequent sequences similar to motif finding. We evaluate the algorithm on several real-world data from different areas, such as the car speed data, the motion capture data, and the weather data. Experiments demonstrate the effectiveness of the proposed algorithm to discover anomalies in real-world time series.