TREND-CONDITIONED RESIDUAL DIFFUSION FOR MULTIVARIATE TIME-SERIES ANOMALY DETECTION

Unsupervised multivariate time-series anomaly detection is challenging in real-world systems because normal observations often exhibit non-stationary trends, local fluctuations, and evolving inter-variable dependencies. Existing reconstruction-based methods usually learn normal patterns directly in the raw sequence space, which may confuse normal trend variations with anomalies and reduce the discriminability of reconstruction errors. To address this issue, we propose a Trend-Conditioned Residual Diffusion framework for multivariate time-series anomaly detection. Instead of recovering the entire sequence from a pure-noise state, the proposed method constructs a smoothed trend state as a structured endpoint and trains a diffusion bridge to recover the fine-grained residual between the original sequence and its trend component. This design shifts the reconstruction target from raw-sequence generation to trend-conditioned residual recovery, thereby providing a more stable normal-pattern baseline for anomaly scoring. To further capture cross-variable normal dependencies, we introduce a channel aggregate-redistribute fusion module that summarizes global channel context and redistributes it to each variable representation. Extensive experiments on five public real-world datasets demonstrate that the proposed method achieves competitive detection performance under commonly used point-adjusted evaluation and remains effective under affiliation-based metrics and detection-delay analysis. Ablation studies further verify the contributions of the diffusion bridge, trend-conditioned residual recovery, and channel-coupling fusion.