Home About me Talks Publications Service Teaching Thesis

Time Series Anomaly Detection, Graduate Course (International Summer School on Internet of Things - ISSIOT, Salerno, Italy)

Lecture 1: An Overview of Anomaly Detection for Time Series

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. The slides of this lecture can be found here.

Lecture 2: A deep dive into time series anomaly detection evaluation measures

Anomaly detection is a fundamental task for time-series analytics with important implications for the downstream performance of many applications. In contrast to other domains where anomaly detection mainly focuses on point-based anomalies (i.e., outliers in standalone observations), anomaly detection for time series is also concerned with range-based anomalies (i.e., outliers spanning multiple observations). Nevertheless, it is common to use traditional point-based information retrieval measures, such as Precision, Recall, and Fscore, to assess the quality of methods by thresholding the anomaly score to mark each point as an anomaly or not. However, mapping discrete labels into continuous data introduces unavoidable shortcomings, complicating the evaluation of range-based contextual and collective anomalies. In this talk, we will dive into the existing evaluation measures to properly compare anomaly detection methods for time series. Finally, we will discuss how evaluation measures can be compared and evaluated. The slides of this lecture can be found here.