Title: Time Series Anomaly Detection: Foundations and Practices

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, 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 will then present new benchmarks capturing diverse domains and applications for the purpose of evaluating anomaly detection methods. We will then conclude on Ensembling and Model Selection for time series anomaly detection, discussing different strategies applicable to automatically selecting the appropriate methods for a specific time series.