Triclustering based outlier-shape score for time series in a fraud detection platform
Résumé
This paper presents a triclustering based outlier-shape score for time series in the context of a fraud detection platform for wholesale traffic for a telecommunications carrier. We propose to use triclustering as an exploration module for outlier shape detection using whole time series. Three main steps compose this approach: (1) projection of data in a new space of time series related features (e.g. derivative), (2) estimation of the density of known normal data using a triclustering method (3) computation of an outlierness score quantifying the distance to the estimator from step (2). We conduct an evaluation of the methodology by focusing on its ability to separate data from different classes. Our preliminary results to assess this approach are very encouraging.
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