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Residual size is not enough for anomaly detection : impr..:

, In: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing,

Yun, Jeong-Han ; Kim, Jonguk ; Hwang, Won-Seok... - p. 87-96 , 2022

Link: https://dl.acm.org/doi/10.1145/3477314.3506990

RT 
T1 <cTitle>Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing</cTitle>: T1 <Title>Residual size is not enough for anomaly detection				: improving detection performance using residual similarity in multivariate time series</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3506990&Exemplar=1&LAN=DE
				
A1 Yun, Jeong-Han
A1 Kim, Jonguk
A1 Hwang, Won-Seok
A1 Kim, Young Geun
A1 Woo, Simon S.
A1 Min, Byung-Gil
PB ACM
YR 2022
K1 anomaly detection
K1 anomaly score
K1 multivariate time-series
K1 nearest neighbor distance
K1 regression model
K1 residual pattern
K1 Information systems
K1 Information retrieval
K1 Evaluation of retrieval results
K1 Retrieval effectiveness
K1 Computing methodologies
K1 Machine learning
K1 Machine learning algorithms
K1 Theory of computation
K1 Design and analysis of algorithms
K1 Data structures design and analysis
K1 Pattern matching
SP 87
OP 96
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3477314.3506990
DO https://dl.acm.org/doi/10.1145/3477314.3506990
SF ELIB - SuUB Bremen

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