Wow! eBook
Event Detection in Time Series (Synthesis Lectures on Data Management)
by: Eduardo Ogasawara (Author), Rebecca Salles (Author),Fabio Porto (Author),Esther Pacitti (Author)
Publisher: Springer
Edition: 2025th
Publication Date: 2025-01-29
Language: English
Print Length: 182 pages
ISBN-10: 3031759400
ISBN-13: 9783031759406
Book Description
This book is dedicated to exploring and explaining time series event detection in databases. The focus is on events, which are pervasive in time series applications where significant changes in behavior are observed at specific points or time intervals. Event detection is a basic function in surveillance and monitoring systems and has been extensively explored over the years, but this book provides a unified overview of the major types of time series events with which researchers should be familiar: anomalies, change points, and motifs. The book starts with basic concepts of time series and presents a general taxonomy for event detection. This taxonomy includes (i) granularity of events (punctual, contextual, and collective), (ii) general strategies (regression, classification, clustering, model-based), (iii) methods (theory-driven, data-driven), (iv) machine learning processing (supervised, semi-supervised, unsupervised), and (v) data management (ETL process). This taxonomy is weaved throughout chapters dedicated to the specific event types: anomaly detection, change-point, and motif discovery. The book discusses state-of-the-art metric evaluations for event detection methods and also provides a dedicated chapter on online event detection, including the challenges and general approaches (static versus dynamic), including incremental and adaptive learning. This book will be of interested to graduate or undergraduate students of different fields with a basic introduction to data science or data analytics.
Editorial Reviews
This book is dedicated to exploring and explaining time series event detection in databases. The focus is on events, which are pervasive in time series applications where significant changes in behavior are observed at specific points or time intervals. Event detection is a basic function in surveillance and monitoring systems and has been extensively explored over the years, but this book provides a unified overview of the major types of time series events with which researchers should be familiar: anomalies, change points, and motifs. The book starts with basic concepts of time series and presents a general taxonomy for event detection. This taxonomy includes (i) granularity of events (punctual, contextual, and collective), (ii) general strategies (regression, classification, clustering, model-based), (iii) methods (theory-driven, data-driven), (iv) machine learning processing (supervised, semi-supervised, unsupervised), and (v) data management (ETL process). This taxonomy is weaved throughout chapters dedicated to the specific event types: anomaly detection, change-point, and motif discovery. The book discusses state-of-the-art metric evaluations for event detection methods and also provides a dedicated chapter on online event detection, including the challenges and general approaches (static versus dynamic), including incremental and adaptive learning. This book will be of interested to graduate or undergraduate students of different fields with a basic introduction to data science or data analytics.
