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1 Ergebnisse
1
An Empirical Analysis of Anomaly Detection Methods for Mult..:
, In:
2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)
,
Li, Dongwen
;
Zhang, Shenglin
;
Sun, Yongqian
... - p. 57-68 , 2023
Link:
https://doi.org/10.1109/ISSRE59848.2023.00014
RT T1 <cTitle>2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)</cTitle>: T1 <Title>An Empirical Analysis of Anomaly Detection Methods for Multivariate Time Series</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10301273&Exemplar=1&LAN=DE A1 Li, Dongwen A1 Zhang, Shenglin A1 Sun, Yongqian A1 Guo, Yang A1 Che, Zeyu A1 Chen, Shiqi A1 Zhong, Zhenyu A1 Liang, Minghan A1 Shao, Minyi A1 Li, Mingjie A1 Liu, Shuyang A1 Zhang, Yuzhi A1 Pei, Dan YR 2023 SN 2332-6549 K1 Solid modeling K1 Web services K1 Computational modeling K1 Time series analysis K1 Software algorithms K1 Training data K1 Data models K1 Multivariate Time Series K1 Anomaly Detection K1 Practical Challenges K1 Empirical Analysis SP 57 OP 68 LK http://dx.doi.org/https://doi.org/10.1109/ISSRE59848.2023.00014 DO https://doi.org/10.1109/ISSRE59848.2023.00014 SF ELIB - SuUB Bremen
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