No Free Lunch But A Cheaper Supper: A General Framework for Streaming Anomaly Detection
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Over the past years, there has been an increased research interest in the problem of detecting anomalies in temporal streaming data. Among the many algorithms proposed every year, there exists no single general method that has been shown to outperform the others across different anomaly types, applications, and datasets. Furthermore, experimental studies conducted using existing methods lack reliability since they attempt to assess the superiority of the algorithms without studying their shared properties and the differences thoroughly. In this paper, we propose SAFARI, a general framework formulated by abstracting and unifying the fundamental tasks in streaming anomaly detection and provides a flexible and extensible anomaly detection procedure. SAFARI helps to facilitate more elaborate algorithm comparisons by allowing to isolate the effects of shared and unique characteristics of different algorithms on the detection performance. Using SAFARI, we have implemented different anomaly detectors and identified a research gap which motivates us to propose a novel learning strategy in this work. Then, we have conducted an extensive evaluation study on 20 detectors that are composed by SAFARI and compared their performances using real-world benchmark datasets with different properties. Finally, we have discussed their benefits and drawbacks in-depth and drawn a set of conclusions to guide future users of SAFARI.
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