Stationary Multi-scale Hierarchical Dilated Graph Convolution for Multivariate Time Series Anomaly Detection
Paper
Liang, Lifang, Qiu, Xuyi, Zhang, Yan, Guan, Donghai, Zhang, Ji and Yuan, Weiwei. 2024. "Stationary Multi-scale Hierarchical Dilated Graph Convolution for Multivariate Time Series Anomaly Detection." 5th International Conference on Big Data and Security (ICBDS 2023). Nanjing, China 22 - 24 Dec 2023 Singapore . Springer. https://doi.org/10.1007/978-981-97-4390-2_5
| Paper/Presentation Title | Stationary Multi-scale Hierarchical Dilated Graph Convolution for Multivariate Time Series Anomaly Detection |
|---|---|
| Presentation Type | Paper |
| Authors | Liang, Lifang, Qiu, Xuyi, Zhang, Yan, Guan, Donghai, Zhang, Ji and Yuan, Weiwei |
| Journal or Proceedings Title | Proceedings of the 5th International Conference on Big Data and Security (ICBDS 2023) |
| Journal Citation | 2100, pp. 52-66 |
| Number of Pages | 15 |
| Year | 2024 |
| Publisher | Springer |
| Place of Publication | Singapore |
| ISBN | 9789819743896 |
| 9789819743902 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-97-4390-2_5 |
| Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-981-97-4390-2_5 |
| Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-981-97-4390-2 |
| Conference/Event | 5th International Conference on Big Data and Security (ICBDS 2023) |
| Event Details | 5th International Conference on Big Data and Security (ICBDS 2023) Delivery In person Event Date 22 to end of 24 Dec 2023 Event Location Nanjing, China |
| Abstract | Anomaly detection within multivariate time series is a pivotal area of study in the field of data mining, playing a crucial role in safeguarding infrastructure integrity. Effective and precise methods for detecting anomalies are essential for users to diagnose issues promptly, thereby preventing significant financial damage. Current methods for anomaly detection often do not adequately account for the multi-scale spatial and temporal relationships present in sequences, and they also overlook the non-stationarity of the sequence. Thus, this paper proposes SMHDG, a novel unsupervised stationary multi-scale hierarchical dilated graph convolution for multivariate time series anomaly detection. The core of this method is to capture multi-scale temporal dependence and feature correlation of multivariable time series by stacking dilated convolution layers and graph convolution. Mean-while, normalization and de-normalization are used to achieve sequence stationarization. Experiments across four authentic datasets have demonstrated that SMHDG outperforms leading baseline in accurately pinpointing anomalies in time series data. Furthermore, ablation experiments confirm the effectiveness of the method's key components. |
| Keywords | anomaly detection; Multi-scale Hierarchical Dilated Graph Convolution; sequence stationarization ; graph learning |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 461299. Software engineering not elsewhere classified |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Nanjing University of Aeronautics and Astronautics, China |
| Chinese Aeronautical Radio Electronics Research Institute, China | |
| University of Southern Queensland |
Permalink -
https://research.usq.edu.au/item/z995z/stationary-multi-scale-hierarchical-dilated-graph-convolution-for-multivariate-time-series-anomaly-detection
31
total views1
total downloads3
views this month0
downloads this month