PVBoost: Posterior-Calibrated Variance Boosting for Time-Series Anomaly Detection

Time-series anomaly detection (TSAD) is typically performed in an unsupervised manner and follows a two-step pipeline: scoring, which quantifies the abnormality of each time step, and thresholding, which converts continuous scores into actionable decisions. Prior work largely emphasizes scoring, leaving thresholding to ad hoc heuristics despite their inherent coupling. This raises a key question: given anomaly scores from a black-box model, how can we refine the scores and select a decision threshold jointly, in a principled, unsupervised manner? To address this question, we propose \model, an unsupervised, parameter-light, and model-agnostic framework that jointly refines black-box anomaly scores and selects a decision threshold through a posterior-variance feedback loop. Mixture modeling estimates anomaly posteriors, which gate variance-based boosting to emphasize informative uncertainty while suppressing noise. Refined scores update the posteriors, whose trajectory also guides threshold selection, unifying refinement and decision-making without labels or access to detector internals. Experiments with $15$ statistical and neural detectors on an established TSAD benchmark comprising $530$ time series across nine domains demonstrate gains in both scoring and detection. In the univariate and multivariate settings, respectively, \model improves average VUS-PR by $4.3\%$ and $11.4\%$ over the original scores and average F-score by $68.3\%$ and $107.3\%$ over widely used three-sigma thresholding of those scores. These findings support coupled refinement and thresholding as a practical approach to more accurate unsupervised TSAD. Our implementation is available at https://anonymous.4open.science/r/PVBoost.