Anomaly Detection in Quasi-Periodic Time Series based on Automatic Data Segmentation and Attentional LSTM-CNN
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Liu, Fan 
ORCID: 0000-0003-2931-1737, Zhou, Xingshe, Cao, Jinli ORCID: 0000-0002-0221-6361, Wang, Zhu ORCID: 0000-0003-2368-8947, Wang, Tianben, Wang, Hua ORCID: 0000-0002-8465-0996 and Zhang, Yanchun ORCID: 0000-0002-5094-5980
(2020)
Anomaly Detection in Quasi-Periodic Time Series based on Automatic Data Segmentation and Attentional LSTM-CNN.
IEEE Transactions on Knowledge and Data Engineering.
ISSN 1041-4347
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| Item type | Article |
| URI | https://vuir.vu.edu.au/id/eprint/42582 |
| DOI | 10.1109/TKDE.2020.3014806 |
| Official URL | https://ieeexplore.ieee.org/document/9161284 |
| Subjects | Current > FOR (2020) Classification > 4602 Artificial intelligence Current > Division/Research > Institute for Sustainable Industries and Liveable Cities |
| Keywords | QTS; anomaly detection framework; feature extraction; classification; long short-term memory network; convolutional neural network |
| Citations in Scopus | 14 - View on Scopus |
| Download/View statistics | View download statistics for this item |
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