Anomaly Detection in Quasi-Periodic Time Series based on Automatic Data Segmentation and Attentional LSTM-CNN

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Liu, F, Zhou, X, Cao, J, Wang, Z, Wang, T, 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 https://doi.org/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 9 - View on Scopus
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