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)
Incoming Non-self Citations Over Time
Authors
- 1. Vincent Jacob (Ecole Polytechnique)
- 2. Yanlei Diao (Ecole Polytechnique)
BibTeX Citation
@article{jacob_vldb25,
title = {{Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains}},
author = {Jacob, Vincent and Diao, Yanlei},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {6},
pages = {1691--1704},
doi = {10.14778/3725688.3725699},
url = {https://doi.org/10.14778/3725688.3725699},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,298 | The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis] | 2026 | SIGMOD | 5.093636e-05 |
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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 |
|---|---|---|---|---|
| 1,029 | Anomaly Detection in Time Series: A Comprehensive Evaluation | 2022 | VLDB | 0.00012557065 |
| 1,534 | Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series | 2021 | VLDB | 0.00010464308 |
| 1,793 | TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data | 2022 | VLDB | 9.7435472e-05 |
| 6,721 | Sintel: A Machine Learning Framework to Extract Insights from Signals | 2022 | SIGMOD | 5.7940977e-05 |
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