Benchmarking Inductive Biases for Multivariate Time-Series Anomaly Detection with a Robust Multi-View Channel-Graph Detector
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Computer Science > Machine Learning
arXiv:2605.28103 (cs)
[Submitted on 27 May 2026]
Title:Benchmarking Inductive Biases for Multivariate Time-Series Anomaly Detection with a Robust Multi-View Channel-Graph Detector
Authors:Junhao Wei, Yanxiao Li, Bidong Chen, Yifu Zhao, Haochen Li, Dexing Yao, Baili Lu, Xudong Ye, Jietian Feng, Sio-Kei Im, Yapeng Wang, Xu Yang
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Abstract: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.
| Subjects: | Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT) |
| Cite as: | arXiv:2605.28103 [cs.LG] |
| (or arXiv:2605.28103v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.28103
arXiv-issued DOI via DataCite (pending registration)
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