Experimental Comparison of Online Anomaly Detection Algorithms

Freeman, Cynthia (Verint Intelligent Self-Service) | Merriman, Jonathan (Verint Intelligent Self-Service) | Beaver, Ian (Verint Intelligent Self-Service) | Mueen, Abdullah (University of New Mexico)

AAAI Conferences 

Anomaly detection methods abound and are used extensively in streaming settings in a wide variety of domains. But a strength can also be a weakness; given the vast number of methods, how can one select the best method for their application? Unfortunately, there is no one best way for all domains. Existing literature is focused on creating new anomaly detection methods or creating large frameworks for experimenting with multiple methods at the same time. As the literature continues to grow, extensive evaluation of every available anomaly detection method is not feasible. To reduce this evaluation burden, in this paper we present a framework to intelligently choose the optimal anomaly detection methods based on the characteristics the time series displays. We provide a comprehensive experimental validation of multiple anomaly detection methods over different time series characteristics to form guidelines. Applying our framework can save time and effort by surfacing the most promising anomaly detection methods instead of experimenting extensively with a rapidly expanding library of anomaly detection methods.

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