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

arXiv:2604.01845v1 Announce Type: new 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 fo

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HyunGi Kim, Jisoo Mok, Hyungyu Lee, Juhyeon Shin, Sungroh Yoon · · 1 min read · 10 views
#cs.LG #cs.AI

arXiv:2604.01845v1 Announce Type: new 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.

Executive Summary

This article proposes CANDI, a novel test-time adaptation framework for multivariate time-series anomaly detection under distribution shift. By selectively adapting to potential false positives while preserving pre-trained knowledge, CANDI significantly improves the performance of MTSAD, achieving up to 14% improvement in AUROC using fewer adaptation samples. The framework consists of a False Positive Mining strategy and a Spatiotemporally-Aware Normality Adaptation module. Extensive experiments demonstrate the effectiveness of CANDI in real-world deployments. This work has significant implications for real-time anomaly detection in critical applications such as financial trading, healthcare, and cybersecurity.

Key Points

  • ▸ Proposes CANDI, a novel test-time adaptation framework for MTSAD under distribution shift
  • ▸ Introduces False Positive Mining strategy to curate adaptation samples
  • ▸ Incorporates Spatiotemporally-Aware Normality Adaptation module for structurally informed model updates

Merits

Strength in Addressing Distribution Shift

CANDI effectively addresses the challenge of distribution shift in MTSAD, which is a critical issue in real-world deployments.

Improved Performance

CANDI achieves significant improvement in AUROC, up to 14%, using fewer adaptation samples, making it a promising solution for real-time anomaly detection.

Flexible and Plug-and-Play Framework

CANDI's modular design allows for easy integration of different adaptation strategies and normality models, making it a flexible and adaptable framework.

Demerits

Limited Evaluation on Real-World Data

While CANDI demonstrates effectiveness on synthetic and benchmark datasets, its performance on real-world data is not extensively evaluated, which may limit its practical applicability.

Potential Overfitting to Specific Domains

CANDI's adaptation strategy may lead to overfitting to specific domains or datasets, which could compromise its generalizability and robustness.

Expert Commentary

The proposed CANDI framework is a significant contribution to the field of anomaly detection, particularly in the context of multivariate time-series data. The authors' innovative approach to test-time adaptation and their emphasis on addressing distribution shift are timely and relevant. However, as with any novel framework, further evaluation and refinement are necessary to ensure its practical applicability and robustness. The authors' extensive experimentation on synthetic and benchmark datasets is commendable, but the lack of real-world data evaluation may limit the framework's immediate adoption. Nevertheless, CANDI's potential for improving the performance of MTSAD and its flexibility make it an exciting development in the field.

Recommendations

  • ✓ Further evaluation and refinement of CANDI on real-world datasets and domains are necessary to ensure its practical applicability and robustness.
  • ✓ The authors should explore the potential of CANDI in addressing other challenges in anomaly detection, such as concept drift and non-stationarity.

Sources

Original: arXiv - cs.LG

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