Anomaly detection in real-valued time series has important applications in many diverse areas. We have developed a general algorithm for detecting anomalies in real-valued time series that is computationally very efficient. Our algorithm is exemplar-based which means a set of exemplars are first learned from a normal time series (i.e. not containing any anomalies) which effectively summarizes all normal windows in the training time series. Anomalous windows of a testing time series can then be efficiently detected using the exemplar-based model.
The provided code implements our hierarchical exemplar learning algorithm, our exemplar-based anomaly detection algorithm, and a baseline brute-force Euclidean distance anomaly detection algorithm. Two simple time series are also provided to test the code.
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