An Interactive Dive into Time-Series Anomaly Detection
Résumé
Anomaly detection is an important problem in data
analytics with applications in many domains. In recent years,
there has been an increasing interest in anomaly detection tasks
applied to time series. In this tutorial, we take a holistic view
of anomaly detection in time series, starting from the core
definitions and taxonomies related to time series and anomaly
types, to an extensive description of the anomaly detection
methods proposed by different communities in the literature. We
explore the literature and the proposed methods by demonstrating systems that help users understand the core computational
steps of some methods and navigate benchmark results. Finally,
we describe the problem of model selection for anomaly detection
and discuss recent experimental results.
Origine | Fichiers produits par l'(les) auteur(s) |
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