EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection
Summary: EasyAD is a modular web engine benchmarking 20 automated time-series anomaly-detection methods and 70 variants on heterogeneous TSB-AD data spanning nine domains. Supports joint accuracy/runtime analysis at dataset and series granularity, plus user data and model-selection experiments. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Qinghua Liu (Ohio State University)
- 2. Seunghak Lee (Meta)
- 3. John Paparrizos (Ohio State University)
BibTeX Citation
@article{liu_vldb25,
title = {{EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection}},
author = {Liu, Qinghua and Lee, Seunghak and Paparrizos, John},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5431--5434},
doi = {10.14778/3750601.3750689},
url = {https://doi.org/10.14778/3750601.3750689},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,482 | TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection | 2025 | VLDB | 5.1708619e-05 |
| 10,469 | HYDRA: A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series | 2026 | SIGMOD | 4.9793485e-05 |
| 10,483 | MUFASA: Fast and Accurate Multivariate Time-Series Clustering | 2026 | SIGMOD | 4.9793485e-05 |
| 10,510 | The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis] | 2026 | SIGMOD | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,005 | Anomaly Detection in Time Series: A Comprehensive Evaluation | 2022 | VLDB | 0.00012590067 |
| 1,937 | TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection | 2022 | VLDB | 9.3387043e-05 |
| 3,298 | Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection | 2022 | VLDB | 7.4447462e-05 |
| 3,726 | Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series | 2023 | VLDB | 7.0701368e-05 |
| 4,103 | AutoOD: Automatic Outlier Detection | 2023 | SIGMOD | 6.8066074e-05 |
| 4,686 | A Demonstration of AutoOD: A Self-Tuning Anomaly Detection System | 2022 | VLDB | 6.4708106e-05 |
| 6,073 | AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data | 2024 | VLDB | 5.8953069e-05 |
| 9,482 | TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection | 2025 | VLDB | 5.1708619e-05 |
| 9,488 | Time-Series Anomaly Detection: Overview and New Trends | 2024 | VLDB | 5.1708619e-05 |
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| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 3,726 | Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series | 2023 | VLDB |
| 2 | 9,626 | Theseus: Navigating the Labyrinth of Time-Series Anomaly Detection | 2022 | VLDB |
| 3 | 1,005 | Anomaly Detection in Time Series: A Comprehensive Evaluation | 2022 | VLDB |
| 4 | 9,488 | Time-Series Anomaly Detection: Overview and New Trends | 2024 | VLDB |
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| 6 | 5,115 | TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms | 2022 | VLDB |
| 7 | 6,073 | AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data | 2024 | VLDB |
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