Motif and Anomaly Discovery of Time Series Based on Subseries Join

Time series motifs are repeated similar subseries in one or multiple time series data. Time series anomalies are unusual subseries in one or multiple time series data. Finding motifs and anomalies in time series data are closely related problems and are useful in many domains, including medicine, motion capture, meteorology, and finance. This work presents a novel approach for both the motif discovery problem and the anomaly detection problem. This approach first uses subseries join to obtain the similarity relationships among subseries of the time series data. Then the motif discovery and anomaly detection problems can be converted to graph-theoretic problems solvable by existing graphtheoretic algorithms. Experiments demonstrate the effectiveness of the proposed approach to discover motifs and anomalies in real-world time series data. Experiments also demonstrate that the proposed approach is efficient to process large time series datasets.