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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
- 12435
- Venue
- VLDB
- Year
- 2021
- Pagerank
- 0.00011058945
- Overall Rank
- 1,634 | 88.64%
- 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 |
| 2,290 |
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data |
2022 |
VLDB |
9.0934125e-05 |
| 2,381 |
TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection |
2022 |
VLDB |
8.9327638e-05 |
| 4,079 |
Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series |
2023 |
VLDB |
6.4663636e-05 |
| 5,777 |
ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection |
2024 |
VLDB |
5.3308813e-05 |
| 6,423 |
AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data |
2024 |
VLDB |
5.0670573e-05 |
| 6,440 |
An Experimental Evaluation of Anomaly Detection in Time Series |
2024 |
VLDB |
5.0603878e-05 |
| 6,448 |
Sintel: A Machine Learning Framework to Extract Insights from Signals |
2022 |
SIGMOD |
5.0587973e-05 |
| 6,901 |
BALANCE: Bayesian Linear Attribution for Root Cause Localization |
2023 |
SIGMOD |
4.8925595e-05 |
| 8,224 |
TSGBench: Time Series Generation Benchmark |
2024 |
VLDB |
4.5552948e-05 |
| 9,087 |
A Demonstration of the Exathlon Benchmarking Platform for Explainable Anomaly Detection |
2021 |
VLDB |
4.3993112e-05 |
| 10,569 |
Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains |
2025 |
VLDB |
4.1945683e-05 |
| 10,637 |
TAB: Unified Benchmarking of Time Series Anomaly Detection Methods |
2025 |
VLDB |
4.1945683e-05 |
| 10,876 |
MLP-Mixer based Masked Autoencoders Are Effective, Explainable and Robust for Time Series Anomaly Detection |
2025 |
VLDB |
4.1945683e-05 |
| 11,094 |
Time-Series Anomaly Detection: Overview and New Trends |
2024 |
VLDB |
4.1945683e-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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| 10,637 |
TAB: Unified Benchmarking of Time Series Anomaly Detection Methods |
2025 |
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4.1945683e-05 |
| 4,079 |
Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series |
2023 |
VLDB |
6.4663636e-05 |
| 10,569 |
Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains |
2025 |
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4.1945683e-05 |
| 10,738 |
TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection |
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| 10,830 |
EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection |
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4.1945683e-05 |
| 11,094 |
Time-Series Anomaly Detection: Overview and New Trends |
2024 |
VLDB |
4.1945683e-05 |
| 1,253 |
Anomaly Detection in Time Series: A Comprehensive Evaluation |
2022 |
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0.00013032074 |
| 7,182 |
TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms |
2022 |
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4.8072409e-05 |
| 6,440 |
An Experimental Evaluation of Anomaly Detection in Time Series |
2024 |
VLDB |
5.0603878e-05 |
| 9,087 |
A Demonstration of the Exathlon Benchmarking Platform for Explainable Anomaly Detection |
2021 |
VLDB |
4.3993112e-05 |