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Activity Number:
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362
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Type:
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Invited
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Date/Time:
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Wednesday, August 9, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statisticians in Defense and National Security
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| Abstract - #305018 |
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Title:
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Monitoring Massive Streams Simultaneously: a Holistic Approach
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Author(s):
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Deepak K. Agarwal*+
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Companies:
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AT&T Labs-Research
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Address:
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180 Park Ave., Florham Park, NJ, 07932,
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Keywords:
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Kalman filter ; prospective ; multiple testing
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Abstract:
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A holistic approach to prospective anomaly detection for massive number of streams is proposed. The method works by building a baseline model to capture normal behavior. Any baseline model that provides a p-value for the observed, relative to the predicted can be used. Anomalies are detected by tracking normal scores derived from p-values. A flexible and fast five-parameter Bayesian model adjusts for multiple testing at each time point. Methods to delete uninformative streams from the monitoring process are also discussed. The method is illustrated on a real application where our baseline model is built using a state space approach.
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