# Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains

arXiv: https://arxiv.org/abs/2503.23060 · HTML: https://mlsanity.com/paper/2503.23060/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 60 references that are in the map.
- First posted on arXiv 29 Mar 2025.

## Builds on (most cited first, 22 of 22)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Learning to Learn Single Domain Generalization](https://mlsanity.com/paper/2003.13216/) (2020)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications](https://mlsanity.com/paper/1802.03903/) (2018)
- [TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2201.07284/) (2022)
- [A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data](https://mlsanity.com/paper/1811.08055/) (2018)
- [Domain Generalization Using a Mixture of Multiple Latent Domains](https://mlsanity.com/paper/1911.07661/) (2019)
- [Efficient Domain Generalization via Common-Specific Low-Rank Decomposition](https://mlsanity.com/paper/2003.12815/) (2020)
- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [Stabilizing Training of Generative Adversarial Networks through Regularization](https://mlsanity.com/paper/1705.09367/) (2017)
- [DIVA: Domain Invariant Variational Autoencoders](https://mlsanity.com/paper/1905.10427/) (2019)
- [DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection](https://mlsanity.com/paper/2306.10347/) (2023)
- [Domain Adaptation for Time Series Under Feature and Label Shifts](https://mlsanity.com/paper/2302.03133/) (2023)
- [Resampled Priors for Variational Autoencoders](https://mlsanity.com/paper/1810.11428/) (2018)
- [Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series](https://mlsanity.com/paper/2010.05073/) (2020)
- [Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization](https://mlsanity.com/paper/1904.12543/) (2019)
- [Multi-Loss Weighting with Coefficient of Variations](https://mlsanity.com/paper/2009.01717/) (2020)
- [Distribution Matching in Variational Inference](https://mlsanity.com/paper/1802.06847/) (2018)
- [Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques](https://mlsanity.com/paper/2206.05876/) (2022)
- [Sintel: A Machine Learning Framework to Extract Insights from Signals](https://mlsanity.com/paper/2204.09108/) (2022)
- [Gauging Variational Inference](https://mlsanity.com/paper/1703.01056/) (2017)

## Abstract (arXiv)

The widespread adoption of digital services, along with the scale and complexity at which they operate, has made incidents in IT operations increasingly more likely, diverse, and impactful. This has led to the rapid development of a central aspect of "Artificial Intelligence for IT Operations" (AIOps), focusing on detecting anomalies in vast amounts of multivariate time series data generated by service entities. In this paper, we begin by introducing a unifying framework for benchmarking unsupervised anomaly detection (AD) methods, and highlight the problem of shifts in normal behaviors that can occur in practical AIOps scenarios. To tackle anomaly detection under domain shift, we then cast the problem in the framework of domain generalization and propose a novel approach, Domain-Invariant VAE for Anomaly Detection (DIVAD), to learn domain-invariant representations for unsupervised anomaly detection. Our evaluation results using the Exathlon benchmark show that the two main DIVAD variants significantly outperform the best unsupervised AD method in maximum performance, with 20% and 15% improvements in maximum peak F1-scores, respectively. Evaluation using the Application Server Dataset further demonstrates the broader applicability of our domain generalization methods.