Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains

Summary: Frames unsupervised multivariate time-series anomaly detection as domain generalization and introduces DIVAD, a domain-invariant VAE to learn representations robust to shifts in normal behavior across heterogeneous AIOps domains. Provides a unifying benchmark and reports 15–20% higher peak F1 on Exathlon with validation on an application-server dataset. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13829
Venue: VLDB
Year: 2025
Pagerank: 4.1945683e-05
Overall Rank: 10,569 | 26.48%
DOI: 10.14778/3725688.3725699

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Authors

1. Vincent Jacob
2. Yanlei Diao

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
1,253	Anomaly Detection in Time Series: A Comprehensive Evaluation	2022	VLDB	0.00013032074
1,634	Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series	2021	VLDB	0.00011058945
2,290	TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data	2022	VLDB	9.0934125e-05
6,448	Sintel: A Machine Learning Framework to Extract Insights from Signals	2022	SIGMOD	5.0587973e-05

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