Beyond Reverse Denoising: Efficiently Detecting Time Series Anomalies through Prototype-Guided Diffusion Inversion

Most diffusion-based time series anomaly detection methods rely on iterative reverse denoising, coupling detection with generative fidelity even though sample generation is not the goal. Expressive denoisers may reconstruct anomalous patterns, while repeated sampling incurs substantial inference overhead. We argue that the distribution-learning capability of diffusion models can instead define an efficient one-class normality criterion. To this end, we propose PiAD, a prototype-guided framework that detects time series anomalies through deterministic DDIM inversion trajectories without reverse denoising or test-time reconstruction. Our key insight is that a noise predictor learned from normal-dominated data defines normal latent dynamics across noise levels: normal windows tend to follow mode-consistent data-to-noise trajectories, whereas anomalous windows exhibit incompatible transitions. Because normal behavior is often multimodal, a single global trajectory prior may conflate distinct normal modes. PiAD therefore combines prototype-conditioned latent diffusion with prototype-specific gaussian trajectory priors and uses training-calibrated Top-$\mathbf{k}$ aggregation to retain the most discriminative deviations across inversion states. Experiments on five real-world datasets show that PiAD consistently outperforms existing diffusion-based approaches while requiring substantially less inference time, memory, and model capacity.