提出统一基准，评估多变量时间序列异常检测方法性能。
Benchmarking Inductive Biases for Multivariate Time-Series Anomaly Detection with a Robust Multi-View Channel-Graph Detector
- 构建跨数据集的统一评测框架，覆盖五类主流方法。
- 新方法在五个数据集上均达最优，宏平均VUS-ROC达0.675。
- 适合关注鲁棒性与跨域泛化能力的研究者参考。
我们开展了一项统一的实验、分析与基准研究，针对多变量时间序列（MTS）异常检测。评估了十种代表性检测器——涵盖统计、重构、关联、频率及通用变换器家族——在五个数据集（SMD、MSL、SMAP、PSM 和 MSDS）上的有效性、效率、鲁棒性及跨数据集泛化能力。所有方法采用相同的窗口化、评分、硬件与评估指标协议。有效性、消融实验与鲁棒性测试使用三个随机种子；跨数据集迁移仅使用种子0，因每个额外种子需250次源-目标评估。基准结果揭示三个方法无关发现：无单一偏置基线占优；绝对扰动下的VUS-ROC比保留率更具信息量；MSDS表现为事件密集的实际部署场景，而非稀疏点异常基准。在此协议下，我们提出 exttt{ours}，一种结合NOTEARS约束有向通道图视图，并可选补丁注意力与时间关联视图的自适应检测器族。 exttt{ours} 在宏平均VUS-ROC上达到0.675，优于第二名的LSTM-AE（+5.1个百分点），整体排名第一，在所有五个数据集上均进入前三。其在MSL和MSDS上的优势虽小，但平均性能与鲁棒性提升显著：在相同三种子鲁棒性协议下，其在噪声、通道丢弃与时间偏移扰动中均获得最高绝对VUS-ROC。我们公开了MSDS预处理协议、配置文件、脚本与种子级指标数据。
原文摘要 · Abstract (English)
We present a unified experiment, analysis, and benchmark study of multivariate time-series (MTS) anomaly detection. Ten family-representative detectors -- spanning statistical, reconstruction, association, frequency, and generic-transformer families -- are evaluated on five datasets (SMD, MSL, SMAP, PSM, and MSDS) under effectiveness, efficiency, robustness, and cross-dataset generalisation. All methods share the same windowing, scoring, hardware, and metric protocols. Effectiveness, ablation, and robustness use three random seeds; cross-dataset transfer uses seed~0 because each extra seed requires $250$ source-target evaluations. The benchmark yields three method-independent findings: no single-bias baseline dominates; absolute perturbation VUS-ROC is more informative than retention ratios; and MSDS behaves as an event-dense deployment workload rather than a sparse point-anomaly benchmark. Under this protocol we also introduce \ours{}, an adaptive detector family combining a NOTEARS-constrained directed channel-graph view with optional patch-attention and temporal-association views. \ours{} achieves the best macro-average VUS-ROC ($0.675$, $+5.1$~pt over the second-best LSTM-AE), ranks first overall, and reaches the top-3 on all five datasets. Its wins on MSL and MSDS are narrow, while its average and robustness gains are larger: under the same three-seed robustness protocol for every method, it obtains the strongest absolute VUS-ROC across noise, channel dropout, and time-shift perturbations. We release the MSDS preprocessing protocol, configurations, scripts, and seed-level metric dumps.
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