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Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series
Summary: Exathlon is the benchmark for explainable anomaly detection over time series, built on Spark traces with six anomaly types. Ground-truth root-cause and extended-effect labels enable AD/ED evaluation and end-to-end pipelines; demonstrated on three techniques.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 12436
- Venue
- VLDB
- Year
- 2021
- Pagerank
- 0.00011048873
- Overall Rank
- 1,640 | 88.61%
- DOI
-
10.14778/3476249.3476307
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 14 of 14 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 2,289 |
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data |
2022 |
VLDB |
9.0922439e-05 |
| 2,381 |
TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection |
2022 |
VLDB |
8.9241557e-05 |
| 4,082 |
Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series |
2023 |
VLDB |
6.4601453e-05 |
| 5,785 |
ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection |
2024 |
VLDB |
5.3257637e-05 |
| 6,419 |
AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data |
2024 |
VLDB |
5.0621949e-05 |
| 6,435 |
An Experimental Evaluation of Anomaly Detection in Time Series |
2024 |
VLDB |
5.0555305e-05 |
| 6,444 |
Sintel: A Machine Learning Framework to Extract Insights from Signals |
2022 |
SIGMOD |
5.0539419e-05 |
| 6,904 |
BALANCE: Bayesian Linear Attribution for Root Cause Localization |
2023 |
SIGMOD |
4.8878659e-05 |
| 8,222 |
TSGBench: Time Series Generation Benchmark |
2024 |
VLDB |
4.5509275e-05 |
| 9,084 |
A Demonstration of the Exathlon Benchmarking Platform for Explainable Anomaly Detection |
2021 |
VLDB |
4.3951172e-05 |
| 10,578 |
Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains |
2025 |
VLDB |
4.1905499e-05 |
| 10,645 |
TAB: Unified Benchmarking of Time Series Anomaly Detection Methods |
2025 |
VLDB |
4.1905499e-05 |
| 10,880 |
MLP-Mixer based Masked Autoencoders Are Effective, Explainable and Robust for Time Series Anomaly Detection |
2025 |
VLDB |
4.1905499e-05 |
| 11,097 |
Time-Series Anomaly Detection: Overview and New Trends |
2024 |
VLDB |
4.1905499e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| Overall Rank |
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Year |
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Pagerank |
| 10,645 |
TAB: Unified Benchmarking of Time Series Anomaly Detection Methods |
2025 |
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4.1905499e-05 |
| 4,082 |
Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series |
2023 |
VLDB |
6.4601453e-05 |
| 10,578 |
Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains |
2025 |
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4.1905499e-05 |
| 10,745 |
TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection |
2025 |
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| 10,834 |
EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection |
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4.1905499e-05 |
| 11,097 |
Time-Series Anomaly Detection: Overview and New Trends |
2024 |
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4.1905499e-05 |
| 1,253 |
Anomaly Detection in Time Series: A Comprehensive Evaluation |
2022 |
VLDB |
0.00013019488 |
| 7,183 |
TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms |
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VLDB |
4.8026282e-05 |
| 6,435 |
An Experimental Evaluation of Anomaly Detection in Time Series |
2024 |
VLDB |
5.0555305e-05 |
| 9,084 |
A Demonstration of the Exathlon Benchmarking Platform for Explainable Anomaly Detection |
2021 |
VLDB |
4.3951172e-05 |