New Trends in Time Series Anomaly Detection

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 tutorial, we take a holistic view on 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. Then, we discuss shortcomings in traditional evaluation measures. Finally, we present new solutions to assess the quality of anomaly detection approaches and new benchmarks capturing diverse domains and applications.