Event Detection in Time Series
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- Hardcover
- Taschenbuch
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Form:Einzelkauf Download
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Sprache:Englisch
49,49 €
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Produktdetails
Format
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Nein
Erscheinungsdatum
28.01.2025
Verlag
SpringerSeitenzahl
170 (Printausgabe)
Dateigröße
8685 KB
Sprache
Englisch
EAN
9783031759413
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.
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