Beyond a Fixed Patch Scale: Scale-Decoupled Multi-Granularity Evidence Fusion for Time-Series Anomaly Detection

Patch-based time-series anomaly detection typically relies on a predefined patch length, thereby restricting anomaly observation to a single temporal receptive field. However, real-world anomalies exhibit substantial heterogeneity in duration and contextual dependency, making a fixed scale prone to scale mismatch. To address this issue, we propose MGF-AD (Multi-Granularity Evidence Fusion for Scale-Uncertain Time-Series Anomaly Detection), which interprets patch length as the temporal granularity of anomaly observation. MGF-AD independently learns scale-specific normal patterns at multiple temporal scales, projects the resulting patch-level anomaly evidence onto a shared timeline via patch-to-point alignment, and performs per-scale score calibration followed by test-label-free fusion. This design explicitly decouples scale-specific normality modeling from cross-scale anomaly decision making. On TSB-AD-U and TSB-AD-M, MGF-AD achieves an overall advantage over the globally best fixed scale on the primary detection metrics and partially closes the performance gap between a globally fixed scale and the per-series optimal scale. Further analysis shows that anomaly evidence from different temporal granularities is complementary, suggesting that replacing fixed-scale selection with cross-scale evidence integration can reduce the sensitivity of patch-based TSAD to a single predefined temporal granularity.