DQE: A Semantic-Aware Evaluation Metric for Time Series Anomaly Detection

Time series anomaly detection (TSAD) is a foundational problem in data mining, aiming to identify observations that deviate significantly from nominal temporal patterns. Driven by escalating data volumes and application complexity, recent research has yielded diverse architectures, including diffusion models, state space models, and large language models (LLMs). However, existing surveys provide limited coverage of these emerging designs and lack a unified treatment of multivariate anomalies, cross-domain unified modeling, metric reliability, and contemporary evaluation challenges. To bridge these gaps, this survey delivers three core contributions: (1) We establish a refined anomaly categorization and a structured taxonomy of paradigms reflecting modern advances, comparing detection mechanisms and charting developmental trajectories across paradigms. (2) We provide an extensive analysis of evaluation methodologies and conduct comprehensive experiments to assess metric fidelity, complemented by an open-source benchmark repository encompassing algorithms, datasets, and evaluation tools. (3) We outline key open challenges, offering forward-looking perspectives on dynamic thresholding, model interpretability, zero-shot transfer, multi-modal alignment, and foundational models for time series. By addressing existing theoretical and empirical limitations, this survey establishes a coherent framework to guide future TSAD research and practical deployments.

Time Series Anomaly Detection has received increasing attention, driven by the growing availability of complex time series data. This surge has led to the development of numerous detection methods, as well as a variety of benchmarks aimed at thoroughly evaluating their performance. However, most existing detectors remain largely agnostic to domain context, overlooking explainability and interpretability. One of the main reasons for this gap is that current benchmarks primarily focus on detection accuracy, and only few of them evaluate spatial explainability. Moreover, no benchmark currently provides sufficiently rich semantic annotations to support the generation of human-understandable interpretations of anomalies. To address these limitations, we introduce SHAD (Scality High-dimensional Anomaly Detection benchmark), a fully annotated benchmark composed of 215 multivariate, high-dimensional time series collected from real-world distributed cloud storage systems operated by Scality. The proposed dataset includes rich contextual information, covering three families of anomalies with varying degrees of severity. As further contribution, we provide a foundation for future work by evaluating baseline methods for Detection, Explainability, and Interpretability, covering all stages of a TSAD pipeline. For Detection, we benchmark a wide range of existing anomaly detectors, testing their effectiveness on the proposed real-world dataset. Then, we consider explainability by evaluating whether measuring the contribution of each dimension in the generated anomaly score can provide accurate anomaly attributions. Finally, for interpretability, we investigate the effectiveness of frozen LLM baselines in localizing and interpreting anomalies.

Point-adjustment (PA), long the default scoring protocol in time-series anomaly detection (TSAD), was shown by Kim et al. (2022) to award near-perfect F1 to random scores. The field migrated to replacement metrics: PA%K, range-based precision/recall, affiliation precision/recall, and Volume-Under-the-Surface (VUS) ROC/PR. But did the fix work? Every robustness check to date is proposer-run or theoretical; no independent, adversarial, SOTA-relative audit on real benchmarks exists. We provide one. We stress-test 12 adopted metrics against trivial and adversarial no-skill score generators on the 250-series UCR Anomaly Archive (primary) plus five further benchmarks (SMD, SMAP, MSL, NAB, PSM), under a pre-registered SOTA-relative gameability criterion (a metric is gamed on a series if a no-skill generator reaches >=90% of the best real detector's score). The fix is only partial: affiliation-F1 (gamed on 99% of series) and every ROC-based metric tested (all roughly 62-64%) are gamed by no-skill detectors, genuinely so (real detectors score 0.97-1.00 on the very series they game), while the PR-based metrics and PA%K resist (14-18%). A decisive, one-directional McNemar split: VUS-ROC is gamed on 119 series where its sibling VUS-PR is not, and the reverse on none. The ROC-over-PR direction replicates across all five further benchmarks and never reverses, but the absolute gameability rate is benchmark-dependent and no single metric stays safe on all six (even VUS-PR is gamed on NAB). We release a pip-installable stress-test harness with all metric implementations vendored at frozen commits, plus a metric-selection decision protocol: prefer a PR-based metric or PA%K, treat affiliation-F1 and the ROC-AUC variants as gameable, and verify per benchmark rather than assuming robustness.