COGNOS: Universal Enhancement for Time Series Anomaly Detection via Constrained Gaussian-Noise Optimization and Smoothing

arXiv:2511.06894 · cs.LG, cs.AI · Submitted 2025-11-10 · Read on arXiv

cs.LG, cs.AI

Submitted: 2025-11-10

Updated: 2026-09-27

Comments: Paper accepted by the 43rd International Conference on Machine Learning

Code: https://github.com/giansha/COGNOS

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

Terminology

• Time Series Anomaly Detection — 17× in this paper
• Transformer — 16× in this paper · explained in 2 episodes
• Regularization — 12× in this paper · explained in 3 episodes
• Calibration — 10× in this paper · explained in 9 episodes
• Adaptive Residual Kalman Smoother — 7× in this paper
• Benchmark — 7× in this paper · explained in 2 episodes
• Mean Squared Error — 7× in this paper
• AUC — 5× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Robustness — 5× in this paper · explained in 6 episodes
• Ablation study — 4× in this paper · explained in 3 episodes
• Attention Mechanism — 4× in this paper · explained in 3 episodes
• Reconstruction Error — 4× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Distribution Shift — 3× in this paper · explained in 10 episodes
• Model-Agnostic — 3× in this paper · explained in 2 episodes
• Wavelet Transform — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Contrastive Learning — 2× in this paper · explained in 8 episodes
• Gaussian-White-Noise Regularized — 2× in this paper
• Heteroscedasticity — 2× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 2× in this paper
• Latent Space — 2× in this paper · explained in 7 episodes
• Maximum Likelihood Estimation — 2× in this paper
• Maximum Mean Discrepancy — 2× in this paper
• Non-Gaussianity — 2× in this paper · explained in 3 episodes
• Power Spectral Density — 2× in this paper
• Representation Learning — 2× in this paper · explained in 3 episodes
• Temporal Correlations — 2× in this paper · explained in 2 episodes
• Volume Under the Surface — 2× in this paper
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Active Regions — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Alarm Fatigue — 1× in this paper
• Alignment — 1× in this paper
• Curiosity and the Soil Moisture Active Passive — 1× in this paper
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 12 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Hybrid Architectures — 1× in this paper · explained in 3 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Kullback-Leibler (KL) Divergence — 1× in this paper · explained in 6 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Mars Science Laboratory — 1× in this paper
• Minimum Mean Square Error — 1× in this paper
• Multi-objective Optimization — 1× in this paper · explained in 5 episodes
• Non-stationarity — 1× in this paper · explained in 2 episodes
• Normalized Innovation Squared — 1× in this paper
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Pooled Server Metrics — 1× in this paper
• Radial Basis Function — 1× in this paper
• Steering — 1× in this paper · explained in 2 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes
• Time Series Forecasting — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks

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