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Assumption-Free Anomaly Detection in Time Series



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Assumption Free Anomaly Detection in Time Series Li Wei Nitin Kumar Venkata Lolla Eamonn Keogh Stefano Lonardi Chotirat Ann Ratanamahatana University of California Riverside Department of Computer Science Engineering Riverside CA 92521 USA wli nkumar vlolla eamonn stelo ratana cs ucr edu Abstract Recent advancements in sensor technology have made it possible to collect enormous amounts of data in real time However because of the sheer volume of data most of it will never be inspected by an algorithm much less a human being One way to mitigate this problem is to perform some type of anomaly novelty interestingness surprisingness detection and flag unusual patterns for further inspection by humans or more CPU intensive algorithms Most current solutions are custom made for particular domains such as ECG monitoring valve pressure monitoring etc This customization requires extensive effort by domain expert Furthermore handcrafted systems tend to be very brittle to concept drift In this demonstration we will show an online anomaly detection system that does not need to be customized for individual domains yet performs with exceptionally high precision recall The system is based on the recently introduced idea of time series bitmaps To demonstrate the universality of our system we will allow testing on independently annotated datasets from domains as diverse as ECGs Space Shuttle telemetry monitoring video surveillance and respiratory data In addition we invite attendees to test our system with any dataset available on the web 1 Introduction Recent advancements in sensor technology have made it possible to collect enormous amounts of data in real time However because of the sheer volume of data most of it is never inspected by an algorithm much less a human being One way to mitigate this problem is to perform some type of anomaly novelty interestingness surprisingness detection and to flag unusual patterns for future inspection by humans or more CPU intensive algorithms



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