SiET: Spatial information enhanced transformer for multivariate time series anomaly detection
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By using sparse and variational autoencoder architectures, TVGeAN can capture intrinsic knowledge from a small training space and generalize it effectively without using complex approaches.
“Furthermore, by using sparse and variational autoencoder architectures, TVGeAN can capture the intrinsic knowledge from a small training space and generalize it effectively without using complex approaches, as is the case with the models that use the dynamic graph approach, e.g., refs.[103-106].”
From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [105]
Another drawback of using dynamic GNN is the long training time required to achieve a stable learning environment.
“Another drawback of using dynamic GNN is the long training time required to achieve a stable learning environment [105].”
From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [105]
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534 · see citing works
Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008) · 2008 Eighth IEEE International Conference on Data Mining · doi:10.1109/icdm.2008.17 · Cited by 6,601 · see citing works
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., & Pei, D. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330672 · Cited by 1,804 · see citing works
Li, Z., Zhao, Y., Han, J., Su, Y., Jiao, R., Wen, X., & Pei, D. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining · doi:10.1145/3447548.3467075 · Cited by 464 · see citing works
Yairi, T., Takeishi, N., Oda, T., Nakajima, Y., Nishimura, N., & Takata, N. (2017) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2017.2671247 · Cited by 185 · see citing works
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Baz, M. (2024) · Mathematics · doi:10.3390/math12213320 · Cited by 1 · see citing works
- Authors
- Xiong, W., Wang, P., Sun, X., & Wang, J.
- Affiliations
- Tianjin University (CN)
- Corresponding author
- Sun, Xiaochen
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2024-07
- Volume
- 296
- Pages
- 111928
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W4396835482
- Cited by
- 32
- References
- 6
- Citations by year
- 2026: 17 · 2025: 12 · 2024: 3
- FWCI
- 4.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Knowledge-Based Systems (publishedVersion)
- Funders
- National Natural Science Foundation of China (72261147707)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- multivariate time series anomaly detection, unsupervised anomaly detection, graph neural networks, geospatial data, attention mechanism, normal distribution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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