ℓ2-type regularization-based unsupervised anomaly detection from temporal data
Seif-Eddine Benkabou, Khalid Benabdeslem, Bruno Canitia · 2017
Temporal data presents challenges and opportunities for machine learning and data mining communities. Recently, the unsupervised anomaly detection task for this kind of data has received much attention. In this paper, we propose a new embedded approach, named ℓ2-DAT, for dealing with this task. Our approach consists in reframing this task as weighting-instance clustering problem based on Ridge regularization and Dynamic Time Warpping for time series data. The anomalous series are then detected by an optimization problem of a new proposed cost function. Extensive experiments on benchmark data sets are carried out for validating our approach and comparing it with other methods of detection.