Bridging the gap: An integrated approach to motif discovery and discord detection in time-series data

Anomaly detection in time-series data is a critical task with implications for healthcare, law enforcement, and smart policing, yet it presents considerable challenges. Traditional methods often require extensive computational resources and exhibit limited success in discerning various anomaly types...

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Vydané v:	Neurocomputing (Amsterdam) Ročník 619; s. 129056
Hlavný autor:	Hu, Wentao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 28.02.2025
Predmet:	Data mining Data security Discord detection Evaluation metrics Motif discovery Predictive policing Unsupervised anomaly detection Predictive policing Data security Motif discovery Unsupervised anomaly detection Data mining Discord detection Evaluation metrics
ISSN:	0925-2312
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