Supervision Recovery for Time Series Anomaly Detection via Context-Anchored Pairing

arXiv:2609.33610 · cs.AI · Submitted 2026-09-27 · Read on arXiv

cs.AI

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/gyf-0623/caps

Terminology

• Benchmark — 29× in this paper · explained in 2 episodes
• Representation Learning — 15× in this paper · explained in 2 episodes
• Ablation — 11× in this paper · explained in 1 episode
• Transformer — 9× in this paper · explained in 2 episodes
• Consistency — 8× in this paper · explained in 3 episodes
• Robustness — 7× in this paper · explained in 5 episodes
• Clustering — 6× in this paper · explained in 2 episodes
• Core — 6× in this paper · explained in 2 episodes
• Adam — 5× in this paper · explained in 2 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Context-Anchored Pair Supervision — 3× in this paper
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Process — 2× in this paper · explained in 2 episodes
• Institutional Review Board — 2× in this paper
• Latent Space — 2× in this paper · explained in 7 episodes
• Wasserstein Distance — 2× in this paper · explained in 4 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Open Challenges in Time Series Anomaly Detection: An Industry Perspective
• LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
• AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Scheme
• FlowTS: Time Series Generation via Rectified Flow

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