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dc.contributor.advisorChan, Philip K.
dc.contributor.authorMahoney, Matthew V.
dc.contributor.authorChan, Philip K.
dc.date.accessioned2013-11-07T21:39:12Z
dc.date.available2013-11-07T21:39:12Z
dc.date.issued2005-02-17
dc.identifier.citationMahoney, M.V., Chan, P.K. (2005). Learning rules for time series anomaly detection (CS-2005-04). Melbourne, FL. Florida Institute of Technology.en_US
dc.identifier.otherCS-2005-04
dc.identifier.urihttp://hdl.handle.net/11141/151
dc.description.abstractWe describe a multi-dimensional time series anomaly detection method in which each point in a test series is required to match the value, slope, and curvature of a point seen in training (with an optional sequential constraint), and a method of generating a model in the form of concise, human-comprehensible, and editable rules. Training time complexity is O(n log n), and testing is O(n). The method generalizes to multiple training series by allowing test points to fall within the range bounded by the training data. We use this approach to detect test failures in Marotta fuel control valves used on the Space Shuttle.en_US
dc.language.isoen_USen_US
dc.rightsCopyright held by authors.en_US
dc.titleLearning rules for time series anomaly detectionen_US
dc.typeTechnical Reporten_US


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