GT-PSSM: Unified Probabilistic Framework for Stochastic Dynamics Modeling and Dependency Learning in Multivariate Time Series Anomaly Detection

arXiv:2609.34161 · stat.ML, cs.LG · Submitted 2026-09-28 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/WonmoKoo/GT-PSSM

Terminology

• Transformer — 33× in this paper · explained in 2 episodes
• Stochastic Dynamics — 11× in this paper · explained in 2 episodes
• Embeddings — 6× in this paper · explained in 2 episodes
• Adjacency Matrix — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• F1 Score — 5× in this paper · explained in 3 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Adaptive Graph — 2× in this paper · explained in 1 episode
• Generalization — 2× in this paper · explained in 13 episodes
• Quantifying Uncertainty — 2× in this paper · explained in 5 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Anomaly Scoring Mechanism — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Concept Drift — 1× in this paper · explained in 4 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Hybrid Architectures — 1× in this paper · explained in 3 episodes
• Hybrid Models — 1× in this paper · explained in 2 episodes
• Pooled Server Metrics — 1× in this paper
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Server Machine Dataset — 1× in this paper
• Steering — 1× in this paper · explained in 2 episodes
• System Identification — 1× in this paper · explained in 3 episodes
• Time Series Forecasting — 1× in this paper · explained in 3 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Layer Normalization
• MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly Detection
• Low Rank Transformer for Multivariate Time Series Anomaly Detection and Localization

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