Coarse Temporal Feedback for Test-Time Adaptation in Streaming Time-Series Anomaly Detection

Test-time adaptation (TTA) can help time-series anomaly detectors cope with evolving normal behavior during deployment. Yet label-free TTA must decide which test observations are safe for adaptation using model-derived signals that may themselves become unreliable under distribution shift. External feedback offers an additional source of adaptation evidence, but in deployment it may be available only periodically and at a coarse temporal granularity. We therefore study a setting in which each completed interval is summarized by one binary response that can influence only future predictions: a negative response certifies the interval as normal, whereas a positive response reveals only that an anomaly occurred somewhere within it. We introduce Feedback-Interval Test-Time Adaptation (FITTA), a minimal adaptation method for this strictly chronological setting. FITTA reprocesses intervals confirmed to be normal for adaptation, penalizes reductions in the interval maximum anomaly score relative to the frozen source detector after positive feedback, and uses the latest response to set the resolution of the next feedback interval. With interval feedback simulated from benchmark labels and the seven benchmark families weighted equally, FITTA raises AUPRC from 0.327 to 0.551 and VUS-PR from 0.248 to 0.506 relative to the frozen detector. These results show that coarse existential feedback can provide substantial adaptation utility for subsequent detection without anomaly localization. A matched factorial analysis further shows that most of this utility comes from adaptation on intervals certified as normal, while positive-evidence preservation and adaptive resolution provide a smaller, non-additive refinement rather than independent additive gains.