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1 Ergebnisse
1
Anomaly Detection in Quasi-Periodic Time Series based on Au..:
, In:
2023 IEEE 39th International Conference on Data Engineering (ICDE)
,
Liu, Fan
;
Zhou, Xingshe
;
Cao, Jinli
... - p. 3777-3778 , 2023
Link:
https://doi.org/10.1109/ICDE55515.2023.00315
RT T1 <cTitle>2023 IEEE 39th International Conference on Data Engineering (ICDE)</cTitle>: T1 <Title>Anomaly Detection in Quasi-Periodic Time Series based on Automatic Data Segmentation and Attentional LSTM-CNN (Extended Abstract)</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10184893&Exemplar=1&LAN=DE A1 Liu, Fan A1 Zhou, Xingshe A1 Cao, Jinli A1 Wang, Zhu A1 Wang, Tianben A1 Wang, Hua A1 Zhang, Yanchun YR 2023 SN 2375-026X K1 Fluctuations K1 Time series analysis K1 Clustering algorithms K1 Logic gates K1 Feature extraction K1 Market research K1 Data engineering K1 Quasi periodic time series K1 anomaly detection K1 data segmentation K1 classification K1 attentional model K1 LSTM K1 CNN SP 3777 OP 3778 LK http://dx.doi.org/https://doi.org/10.1109/ICDE55515.2023.00315 DO https://doi.org/10.1109/ICDE55515.2023.00315 SF ELIB - SuUB Bremen
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