Disclosed are a framework and method for selecting an anomaly detection method for each of a plurality of class of time series based on characteristics a time series example that represents an expected form of data. The method provides classification of a given time series into one of known classes based on expected properties of the time series, filtering the set of possible detection methods based on the time series class, evaluating the remaining detection methods on the given time series using the specific evaluation metric and selecting and returning a recommended anomaly detection method based on the specific evaluation metric.
FRAMEWORK FOR THE AUTOMATED DETERMINATION OF CLASSES AND ANOMALY DETECTION METHODS FOR TIME SERIES
RAHMEN ZUR AUTOMATISIERTEN BESTIMMUNG VON KLASSEN UND ANOMALIEDETEKTIONSVERFAHREN FÜR ZEITREIHEN
CADRE POUR LA DÉTERMINATION AUTOMATIQUE DE CLASSES ET PROCÉDÉS DE DÉTECTION D'ANOMALIE POUR SÉRIES TEMPORELLES
2020-03-18
Patent
Electronic Resource
English
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