Uni-directional graph structure learning-based multivariate time series anomaly detection with dynamic prior knowledge
Author:
Funder
Open Fund of Key Laboratory of Safety Control of Bridge Engineering, Ministry of Education
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s13042-024-02212-5.pdf
Reference30 articles.
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2. Chen Z, Chen D, Zhang X et al (2022) Learning graph structures with transformer for multivariate time-series anomaly detection in iot. IEEE Internet of Things J 9(12):9179–9189. https://doi.org/10.1109/JIOT.2021.3100509
3. Deng A, Hooi B (2021) Graph neural network-based anomaly detection in multivariate time series. In: Thirty-Fifth AAAI Conference on artificial intelligence, pp 4027–4035, https://doi.org/10.1609/AAAI.V35I5.16523
4. Goh J, Adepu S, Junejo KN, et al (2016) A dataset to support research in the design of secure water treatment systems. In: Critical information infrastructures security: 11th International Conference, CRITIS, pp 88–99, https://doi.org/10.1007/978-3-319-71368-7_8
5. Han S, Woo SS (2022) Learning sparse latent graph representations for anomaly detection in multivariate time series. In: Proceedings of the 28th ACM SIGKDD Conference on knowledge discovery and data mining, pp 2977–2986, https://doi.org/10.1109/JIOT.2021.3100509
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