![[2602.20468] CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.20468] CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection
Summary
The CGSTA framework enhances multivariate time-series anomaly detection by utilizing dynamic layered graphs and stability-aware alignment to improve accuracy and reduce false alarms.
Why It Matters
As industries increasingly rely on multivariate time-series data for monitoring and control, effective anomaly detection becomes crucial. The CGSTA framework addresses common pitfalls in existing methods, such as noise interference and overfitting, thereby improving reliability in critical applications.
Key Takeaways
- CGSTA introduces Dynamic Layered Graph Construction for better representation of variable relations.
- Contrastive Discrimination across Scales enhances learning by aligning multiple views of data.
- Stability-Aware Alignment helps suppress noise by maintaining a stable reference from normal data.
- The framework shows optimal performance on benchmarks like PSM and WADI.
- CGSTA is comparable to existing methods on SWaT and SMAP, indicating its robustness.
Computer Science > Machine Learning arXiv:2602.20468 (cs) [Submitted on 24 Feb 2026] Title:CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection Authors:Zhongpeng Qi, Jun Zhang, Wei Li, Zhuoxuan Liang View a PDF of the paper titled CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection, by Zhongpeng Qi and 2 other authors View PDF HTML (experimental) Abstract:Multivariate time-series anomaly detection is essential for reliable industrial control, telemetry, and service monitoring. However, the evolving inter-variable dependencies and inevitable noise render it challenging. Existing methods often use single-scale graphs or instance-level contrast. Moreover, learned dynamic graphs can overfit noise without a stable anchor, causing false alarms or misses. To address these challenges, we propose the CGSTA framework with two key innovations. First, Dynamic Layered Graph Construction (DLGC) forms local, regional, and global views of variable relations for each sliding window; rather than contrasting whole windows, Contrastive Discrimination across Scales (CDS) contrasts graph representations within each view and aligns the same window across views to make learning structure-aware. Second, Stability-Aware Alignment (SAA) maintains a per-scale stable reference learned from normal data and guides the current window's fast-changing graphs toward it to suppress noise. We...