GSLAD: Prototype-Regularized Graph Structure Learning for Multivariate Time Series Anomaly Detection

arXiv:2609.15483 · cs.LG · Submitted 2026-09-14 · Read on arXiv

cs.LG

Submitted: 2026-09-14

Updated: 2026-09-14

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• AUC — 24× in this paper · explained in 2 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Regularization — 4× in this paper · explained in 3 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Time Series Forecasting — 2× in this paper · explained in 3 episodes
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Graph Neural Network (GNN) — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Pooled Server Metrics — 1× in this paper
• Receiver Operating Characteristic — 1× in this paper
• Tennessee Eastman Process — 1× in this paper

Sources

• A Survey on Graph Structure Learning: Progress and Opportunities

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