针对时间序列异常检测在分布偏移下的性能下降问题，提出一种精准筛选假阳性样本的在线自适应方法。
CANDI: Curated Test-Time Adaptation for Multivariate Time-Series Anomaly Detection Under Distribution Shift
- 通过异常分数与潜在空间相似性筛选待适应样本，避免无效更新。
- 在分布偏移下使AUROC提升最高达14%，且仅需少量测试样本。
- 适用于工业监控等需快速响应分布变化的实时异常检测场景。
多变量时间序列异常检测（MTSAD）旨在识别多变量时间序列中偏离正常模式的数据，在实际应用中至关重要。然而，真实部署中普遍存在分布偏移，导致预训练异常检测器性能严重下降。测试时自适应（TTA）通过仅使用无标签测试数据实时更新模型，是应对该挑战的有前景方案。本文提出CANDI（Curated test-time adaptation for multivariate time-series ANomaly detection under DIstribution shift），一种新型TTA框架，可选择性地识别并适应潜在假阳性样本，同时保留预训练知识。CANDI引入虚假阳性挖掘（FPM）策略，基于异常分数和潜在空间相似性筛选适配样本，并集成即插即用的时空感知正常性自适应（SANA）模块，实现结构感知的模型更新。大量实验表明，CANDI在分布偏移下显著提升MTSAD性能，最高使AUROC提升14%，且所需适配样本更少。
原文摘要 · Abstract (English)
Multivariate time-series anomaly detection (MTSAD) aims to identify deviations from normality in multivariate time-series and is critical in real-world applications. However, in real-world deployments, distribution shifts are ubiquitous and cause severe performance degradation in pre-trained anomaly detector. Test-time adaptation (TTA) updates a pre-trained model on-the-fly using only unlabeled test data, making it promising for addressing this challenge. In this study, we propose CANDI (Curated test-time adaptation for multivariate time-series ANomaly detection under DIstribution shift), a novel TTA framework that selectively identifies and adapts to potential false positives while preserving pre-trained knowledge. CANDI introduces a False Positive Mining (FPM) strategy to curate adaptation samples based on anomaly scores and latent similarity, and incorporates a plug-and-play Spatiotemporally-Aware Normality Adaptation (SANA) module for structurally informed model updates. Extensive experiments demonstrate that CANDI significantly improves the performance of MTSAD under distribution shift, improving AUROC up to 14% while using fewer adaptation samples.
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