Anomaly Detection of Industrial Data Based on Multivariate Multi Scale Analysis
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9893-7_7
Reference19 articles.
1. Zhao, P., Chang, X., Wang, M.: A novel multivariate time-series anomaly detection approach using an unsupervised deep neural network. IEEE Access 9, 109025–109041 (2021)
2. Schmidl, S., Wenig, P., Papenbrock, T.: Anomaly detection in time series: a comprehensive evaluation. Proc. VLDB Endow. 15(9), 1779–1797 (2022)
3. Amarbayasgalan, T., Pham, V.H., Theera-Umpon, N., et al.: Unsupervised anomaly detection approach for time-series in multi-domains using deep reconstruction error. Symmetry 12(8), 1251 (2020)
4. Deng, A., Hooi, B.: Graph neural network-based anomaly detection in multivariate time series. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5, pp. 4027–4035 (2021)
5. Guan, S., Zhao, B., Dong, Z., Gao, M., He, Z.: GTAD: graph and temporal neural network for multivariate time series anomaly detection. Entropy 24(6), 759 (2022)
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