Data to 7 Oct 2026

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

arXiv 1802.03903 ↗First posted 12 Feb 2018cs.LG stat.MLcited by 223

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Abstract (arXiv)

To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation. However, anomaly detection for these seasonal KPIs with various patterns and data quality has been a great challenge, especially without labels. In this paper, we proposed Donut, an unsupervised anomaly detection algorithm based on VAE. Thanks to a few of our key techniques, Donut greatly outperforms a state-of-arts supervised ensemble approach and a baseline VAE approach, and its best F-scores range from 0.75 to 0.9 for the studied KPIs from a top global Internet company. We come up with a novel KDE interpretation of reconstruction for Donut, making it the first VAE-based anomaly detection algorithm with solid theoretical explanation.

Key facts. Cited by 223 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Feb 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 36; 2022: 33; 2023: 54; 2024: 35; 2025: 39; 2026: 26.

223 papers.

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2021TS2Vec: Towards Universal Representation of Time Seriescited by 463
2024MOMENT: A Family of Open Time-series Foundation Modelscited by 453
2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 387
2023FITS: Modeling Time Series with 10k Parameterscited by 185
2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 130
2023DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detectioncited by 103
2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 95
2022Local Evaluation of Time Series Anomaly Detection Algorithmscited by 79
2021An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Seriescited by 68
2023Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detectioncited by 66
2025TAB: Unified Benchmarking of Time Series Anomaly Detection Methodscited by 58
2023A Survey on Time-Series Pre-Trained Modelscited by 52

38 more

2023TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learningcited by 51
2023Soft Contrastive Learning for Time Seriescited by 49
2022TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysiscited by 48
2021Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detectioncited by 42
2021Neural Contextual Anomaly Detection for Time Seriescited by 42
2021Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encodercited by 39
2021Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Versioncited by 35
2024TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Modelcited by 33
2025Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysiscited by 32
2021Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filteringcited by 31
2022On the Importance of Building High-quality Training Datasets for Neural Code Searchcited by 30
2024Universal Time-Series Representation Learning: A Surveycited by 28
2024Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detectioncited by 26
2021Merlion: A Machine Learning Library for Time Seriescited by 25
2023AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Schemecited by 25
2021CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesiscited by 24
2022HyperTime: Implicit Neural Representation for Time Seriescited by 24
2022Unsupervised Model Selection for Time-series Anomaly Detectioncited by 24
2023Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Seriescited by 24
2021Distribution-Aware Testing of Neural Networks Using Generative Modelscited by 22
2024Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspectivecited by 22
2024TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Modelscited by 22
2024KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networkscited by 20
2022Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?cited by 19
2022Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Versioncited by 19
2021DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applicationscited by 18
2022Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detectioncited by 18
2023Graph Anomaly Detection in Time Series: A Surveycited by 18
2021DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behaviorcited by 17
2023Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detectioncited by 17
2023Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstructioncited by 17
2023When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detectioncited by 17
2024Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?cited by 17
2022Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detectioncited by 16
2022TiSAT: Time Series Anomaly Transformercited by 15
2023CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detectioncited by 15
2023Explainable Time Series Anomaly Detection using Masked Latent Generative Modelingcited by 15
2024MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysiscited by 15

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