DeDe: Time-Series Deep Decomposition for Explainable Anomaly Detection

Industrial applications of time-series anomaly detection (TSAD) require both high accuracy and strong explainability to support reliable business decision-making. However, few existing TSAD methods satisfy these two requirements simultaneously. To address this challenge, we propose an end-to-end model named Time-Series Deep Decomposition (DeDe) for explainable TSAD. DeDe decomposes a time series into trend, seasonal, and residual components while modeling latent normal structure. Each component is encoded with a multi-scale Transformer, and a forecasting objective augmented with contrastive learning encourages a Stable Latent Manifold Structure (SLMS) of normal dynamics. One-step forecasting error and deviations from the component-wise SLMSs are combined into a point-wise anomaly score. Component-level contributions provide interpretable diagnostic attribution. Experiments on 350 real-world evaluation time series from TSB-AD-U show improvements over the evaluated baselines across multiple metrics. DeDe has also been deployed at scale in industrial production, reliably monitoring millions of time series each day while consistently achieving high recall and precision in real-world operations, demonstrating both strong practical effectiveness and operational robustness.