|Article title||SEARCH FOR ANOMALIES IN TIME SERIES TECHNOLOGICAL DATABASES|
|Authors||A.N. Shabelnikov, V.A. Shabelnikov|
|Section||SECTION V. MODELING OF COMPLEX SYSTEMS|
|Month, Year||04, 2008 @en|
|Abstract||The version of common methodology for knowledge discovery in time series databases, illustrated with the samples of anomaly detection is considered in this paper. The knowledge discovery process involves data refining and filtering as well as features generation and association rules set extraction, that can be used for anomaly detection and further time series behavior forecasting. The method is based on the use of information-theoretic ap- proach of knowledge discovery comprising the ideas of time series linguistic approximation.|
|Keywords||knowledge discovery, databases, time series, association rules, linguistic approximation.|
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