CANDI: Curated Test-Time Adaptation for Multivariate Time-Series Anomaly Detection Under Distribution Shift

• URL: http://arxiv.org/abs/2604.01845v1
• Date: Thu, 02 Apr 2026 10:01:11 GMT
• Title: CANDI: Curated Test-Time Adaptation for Multivariate Time-Series Anomaly Detection Under Distribution Shift
• Abstract summary: Test-time adaptation (TTA) updates a pre-trained model on-the-fly using only unlabeled test data.<n>In this study, we propose CANDI, a novel TTA framework that selectively identifies and adapts to potential false positives while preserving pre-trained knowledge.
• Score: 48.29742059729268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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