Title: ICDiffAD: Implicit Conditioning Diffusion Model for Time Series Anomaly Detection
Primary area: learning on time series and dynamical systems
Keywords: Time Series, Anomaly Detection, Diffusion Model, Implicit Conditioning

Abstract: Time series anomaly detection (TSAD) faces critical challenges from intrinsic data noisiness and temporal heterogeneity, which undermine the reconstruction fidelity of prevailing generative approaches. 
While diffusion models offer theoretical advantages in capturing complex temporal dynamics, their inherent stochasticity introduces irreducible variance in reconstructions.
We present the ICDiffAD, a novel method that synergizes adaptive noise scheduling with semi-deterministic generation to address these limitations. ICDiffAD introduces two key innovations: 
(1) an *SNR Scheduler* that governs training through quantifiable noise scales, enabling robust learning of normative patterns across non-stationary regimes; and 
(2) an *SNR Implicit Conditioning Mechanism* that initializes reverse diffusion from partially corrupted inputs, preserving signal coherence while attenuating anomalous components. 
This dual strategy ensures high-fidelity reconstructions aligned with the input’s manifold, reconciling generative flexibility with detection accuracy. 
Across five multivariate benchmarks, ICDiffAD improves the F1 score by 20.2\% and reduces false positives by 60.23\% compared to existing diffusion model-based TSAD methods.

TL;DR: We propose a fix to current diffusion models in time series anomaly detection, guided by Signal to Noise Ratio both in training and inference, improving current Diffusion Models by 20.2% F1 Scores.