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TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection
Summary: TSB-AutoAD: taxonomy plus benchmark (20 methods, 70 variants) for automated time-series anomaly detection across nine domains, organizing approaches into selection, ensembling, and generation. Extensive evaluation shows many methods fail to beat random; naive ensembling is accurate but costly, dataset-driven methods break OOD; proposes selective ensembling to balance accuracy and efficiency.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 14052
- Venue
- VLDB
- Year
- 2025
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,745 | 25.33%
- DOI
-
10.14778/3749646.3749699
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No non-self incoming citations found for this paper in this database.
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 22 of 22 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 159 |
LOF: Identifying Density-Based Local Outliers |
2000 |
SIGMOD |
0.00040135453 |
| 697 |
Efficient Algorithms for Mining Outliers from Large Data Sets |
2000 |
SIGMOD |
0.00017964755 |
| 1,253 |
Anomaly Detection in Time Series: A Comprehensive Evaluation |
2022 |
VLDB |
0.00013019488 |
| 1,510 |
k-Shape: Efficient and Accurate Clustering of Time Series |
2015 |
SIGMOD |
0.00011588558 |
| 2,032 |
SAND: Streaming Subsequence Anomaly Detection |
2021 |
VLDB |
9.7320795e-05 |
| 2,381 |
TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection |
2022 |
VLDB |
8.9241557e-05 |
| 2,619 |
Decomposed Bounded Floats for Fast Compression and Queries |
2021 |
VLDB |
8.4427442e-05 |
| 2,646 |
Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series |
2020 |
VLDB |
8.3751681e-05 |
| 3,946 |
Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection |
2022 |
VLDB |
6.6036232e-05 |
| 4,062 |
GRAIL: Efficient Time-Series Representation Learning |
2019 |
VLDB |
6.4792249e-05 |
| 4,455 |
AutoOD: Automatic Outlier Detection |
2023 |
SIGMOD |
6.1644904e-05 |
| 4,854 |
Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures |
2020 |
SIGMOD |
5.8707943e-05 |
| 6,311 |
VergeDB: A Database for IoT Analytics on Edge Devices |
2021 |
CIDR |
5.1112212e-05 |
| 6,366 |
Good to the Last Bit: Data-Driven Encoding with CodecDB |
2021 |
SIGMOD |
5.0892171e-05 |
| 6,419 |
AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data |
2024 |
VLDB |
5.0621949e-05 |
| 8,090 |
PIDS: Attribute Decomposition for Improved Compression and Query Performance in Columnar Storage |
2020 |
VLDB |
4.5853298e-05 |
| 9,299 |
Theseus: Navigating the Labyrinth of Time-Series Anomaly Detection |
2022 |
VLDB |
4.356626e-05 |
| 9,334 |
Odyssey: An Engine Enabling The Time-Series Clustering Journey |
2023 |
VLDB |
4.351469e-05 |
| 10,476 |
A Structured Study of Multivariate Time-Series Distance Measures |
2025 |
SIGMOD |
4.1905499e-05 |
| 10,746 |
Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods |
2025 |
VLDB |
4.1905499e-05 |
| 10,834 |
EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection |
2025 |
VLDB |
4.1905499e-05 |
| 11,237 |
Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances |
2023 |
VLDB |
4.1905499e-05 |
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AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data |
2024 |
VLDB |
5.0621949e-05 |
| 2,381 |
TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection |
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VLDB |
8.9241557e-05 |