In this book, we will provide a holistic view on anomaly detection in time series, starting from the core definitions and taxonomies related to time series and anomaly types to a detailed description of the anomaly detection methods proposed by different communities in the literature.

This book presents a novel, comprehensive, process-centric taxonomy of detection methods within each category. We will present a comprehensive range of algorithms in the literature and group them into families of algorithms with similar approaches. We will also cover topics related to evaluating time series anomaly detection and automatic solutions, such as model selection and ensembling. While discussing all these topics, we aim to provide specific examples to illustrate several problems and how some methods work. 

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, Université Paris Cité, EDF Research Lab, and École Polytechnique. 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.

John Paparrizos is an assistant professor at The Ohio State University, leading The DATUM Lab. His research focuses on adaptive solutions for data-intensive and machine-learning applications. His doctoral work was recognized at the 2019 ACM SIGKDD Doctoral Dissertation Award competition. He has also received the inaugural ACM SIGMOD Research Highlight Award, a NetApp Faculty Award, and the 2023 IEEE TCDE Rising Star Award. His ideas have been widely adopted across scientific areas, Fortune 100-500 companies (e.g., Exelon and Nokia), and organizations such as ESA.

Themis Palpanas is an elected Senior Member of the French University Institute (IUF), and Distinguished Professor of computer science at Université Paris Cite (France). He has authored 14 patents, received 3 best paper awards and the IBM SUR award, has been Program Chair for VLDB 2025 and IEEE BigData 2023, and General Chair for VLDB 2013. He has been working in the fields of Data Series Management and Analytics for more than 15 years, and has developed several of the state-of-the-art techniques. He has delivered 19 tutorials in top conferences.