Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate

arXiv cs.LG Papers
time-series anomaly-detection benchmarks multivariate univariate diagnostics

Summary

This paper introduces a diagnostic framework for multivariate time series anomaly detection benchmarks and finds that labeled anomalies are mostly detectable from individual channels, challenging the need for cross-channel modeling. The authors call for more structurally diverse evaluation sets.

arXiv:2606.02670v1 Announce Type: new Abstract: Many recent multivariate time series anomaly detection (MT-SAD) models incorporate cross-channel modeling, under the implicit assumption that the structure of anomalies may be spread across multiple channels. We evaluate this assumption on eight widely used public benchmarks by introducing a per-segment diagnostic framework that flags, for each labeled anomaly, whether at least one channel deviates individually from its normal history, whether the cross-channel correlation structure changes, or both. The framework shows that no crosschannel rupture occurs without an accompanying univariate deviation across a range of reasonable thresholds. A complementary metric also reveals that on six of the eight benchmarks, at least half of the labeled anomaly segments deviate univariately on 79% to 100% of their timesteps, reaching 100% on three of these datasets. To verify that our framework captures cross-channel structure when present, we construct synthetic data of phase-shifted sinusoidal channels with shared noise. Each anomalous segment is altered through one of two channelwise corruptions that preserve the per-channel marginal distribution while breaking cross-channel structure, and our framework correctly characterizes these segments as cross-channel-only. On these data, channel-dependent (CD) models successfully exploit the cross-channel signal whereas channel-independent (CI) ones fail. The CI/CD comparison of a recent SOTA detector on real benchmarks further confirms that CD modeling brings no measurable gain. We conclude that current MTSAD benchmarks are unsuitable for validating cross-channel modeling capabilities, and we call for the development of more structurally diverse evaluation sets. The code for this study is publicly available.
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# Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate
Source: [https://arxiv.org/abs/2606.02670](https://arxiv.org/abs/2606.02670)
[View PDF](https://arxiv.org/pdf/2606.02670)

> Abstract:Many recent multivariate time series anomaly detection \(MT\-SAD\) models incorporate cross\-channel modeling, under the implicit assumption that the structure of anomalies may be spread across multiple channels\. We evaluate this assumption on eight widely used public benchmarks by introducing a per\-segment diagnostic framework that flags, for each labeled anomaly, whether at least one channel deviates individually from its normal history, whether the cross\-channel correlation structure changes, or both\. The framework shows that no crosschannel rupture occurs without an accompanying univariate deviation across a range of reasonable thresholds\. A complementary metric also reveals that on six of the eight benchmarks, at least half of the labeled anomaly segments deviate univariately on 79% to 100% of their timesteps, reaching 100% on three of these datasets\. To verify that our framework captures cross\-channel structure when present, we construct synthetic data of phase\-shifted sinusoidal channels with shared noise\. Each anomalous segment is altered through one of two channelwise corruptions that preserve the per\-channel marginal distribution while breaking cross\-channel structure, and our framework correctly characterizes these segments as cross\-channel\-only\. On these data, channel\-dependent \(CD\) models successfully exploit the cross\-channel signal whereas channel\-independent \(CI\) ones fail\. The CI/CD comparison of a recent SOTA detector on real benchmarks further confirms that CD modeling brings no measurable gain\. We conclude that current MTSAD benchmarks are unsuitable for validating cross\-channel modeling capabilities, and we call for the development of more structurally diverse evaluation sets\. The code for this study is publicly available\.

## Submission history

From: Marc Pinet \[[view email](https://arxiv.org/show-email/637a5376/2606.02670)\] \[via CCSD proxy\] **\[v1\]**Mon, 1 Jun 2026 11:42:35 UTC \(153 KB\)

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