Invited Talk "Anomaly detection in time series"
Abstract
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 talk, we take a holistic view of anomaly detection in time series, discussing the challenges and research opportunities in this field. In addition, we will focus on the challenges related to anomaly detection in heterogeneous time series datasets, as well as on the new research opportunities related to Model Selection and Ensembling.
Paul Boniol, INRIA
Paul Boniol is a researcher at Inria, member of the VALDA project-team which is a joint team between Inria Paris, École Normale Supérieure, and CNRS. Previously, he worked at ENS Paris-Saclay (Centre Borelli), Université Paris Cité, EDF Research lab, and Ecole Polytechnique (LIX). His research interests lie between data analytics, machine learning, and time-series analysis. His Ph.D. dissertation focused on subsequence anomaly detection and time-series classification, and won several PhD awards, including the prestigious Paul Caseau Prize, supported by the Academy of Sciences of France. His work has been published in the top data management and data mining venues.