Variate Associated Domain Adaptation for Unsupervised Multivariate Time Series Anomaly Detection
Article
He, Yifan, Bian, Yatao, Ding, Xi, Wu, Bingzhe, Guan, Jihong, Zhang, Ji and Zhou, Shuigeng. 2024. "Variate Associated Domain Adaptation for Unsupervised Multivariate Time Series Anomaly Detection." ACM Transactions on Knowledge Discovery from Data. 18 (8). https://doi.org/10.1145/3663573
| Article Title | Variate Associated Domain Adaptation for Unsupervised Multivariate Time Series Anomaly Detection |
|---|---|
| ERA Journal ID | 17737 |
| Article Category | Article |
| Authors | He, Yifan, Bian, Yatao, Ding, Xi, Wu, Bingzhe, Guan, Jihong, Zhang, Ji and Zhou, Shuigeng |
| Journal Title | ACM Transactions on Knowledge Discovery from Data |
| Journal Citation | 18 (8) |
| Article Number | 187 |
| Number of Pages | 24 |
| Year | 2024 |
| Publisher | Association for Computing Machinery (ACM) |
| Place of Publication | United States |
| ISSN | 1556-4681 |
| 1556-472X | |
| Digital Object Identifier (DOI) | https://doi.org/10.1145/3663573 |
| Web Address (URL) | https://dl.acm.org/doi/10.1145/3663573 |
| Abstract | Multivariate Time Series Anomaly Detection (MTS-AD) is crucial for the effective management and maintenance of devices in complex systems, such as server clusters, spacecrafts, and financial systems, and so on. However, upgrade or cross-platform deployment of these devices will introduce the issue of cross-domain distribution shift, which leads to the prototypical problem of domain adaptation for MTS-AD. Compared with general domain adaptation problems, MTS-AD domain adaptation presents two peculiar challenges: (1) the dimensions of data from the source domain and the target domain are usually different, so alignment without losing any information is necessary; and (2) the association between different variates plays a vital role in the MTS-AD task, which is overlooked by traditional domain adaptation approaches. Aiming at addressing the above issues, we propose a Variate Associated Domain Adaptation Method Combined with a Graph Deviation Network (VANDA) for MTS-AD, which includes two major contributions. First, we characterize the intra-domain variate associations of the source domain by a graph deviation network (GDN), which can share parameters across domains without dimension alignment. Second, we propose a sliding similarity to measure the inter-domain variate associations and perform joint training by minimizing the optimal transport distance between source and target data for transferring variate associations across domains. VANDA achieves domain adaptation by transferring both variate associations and GDN parameters from the source domain to the target domain. We construct two pairs of MTS-AD datasets from existing MTS-AD data and combine three domain adaptation strategies with six MTS-AD backbones as the benchmark methods for experimental evaluation and comparison. Extensive experiments demonstrate the effectiveness of our approach, which outperforms the benchmark methods, and significantly improves the AD performance of the target domain by effectively utilizing the source domain knowledge. |
| Keywords | anomaly detection; multivariate time series; domain adaptation; optimal transport |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 460999. Information systems not elsewhere classified |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Fudan University, China |
| Tencent AI Lab, China | |
| Tongji University, China | |
| University of Southern Queensland |
Permalink -
https://research.usq.edu.au/item/z99wq/variate-associated-domain-adaptation-for-unsupervised-multivariate-time-series-anomaly-detection
28
total views0
total downloads4
views this month0
downloads this month