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Time-Series Anomaly Detection: Overview and New Trends
Summary: Holistic tutorial surveying time-series anomaly detection from classical stats to modern ML/deep methods, highlighting domain-specific failure modes and lack of one-size-fits-all detectors. Contributions: new taxonomy, critique/advances in benchmarking and evaluation, and interactive tools for algorithm exploration and automated detection pipelines.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13624
- Venue
- VLDB
- Year
- 2024
- Pagerank
- 4.1905499e-05
- Overall Rank
- 11,097 | 22.88%
- DOI
-
10.14778/3685800.3685842
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 1,253 |
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 |
| 1,640 |
Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series |
2021 |
VLDB |
0.00011048873 |
| 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 |
| 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 |
| 4,082 |
Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series |
2023 |
VLDB |
6.4601453e-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 |
| 6,115 |
GraphAn: Graph-based Subsequence Anomaly Detection |
2020 |
VLDB |
5.1995458e-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 |
| 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 |
| 11,237 |
Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances |
2023 |
VLDB |
4.1905499e-05 |
| 13,274 |
SAND in Action: Subsequence Anomaly Detection for Streams |
2021 |
VLDB |
- |
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MLP-Mixer based Masked Autoencoders Are Effective, Explainable and Robust for Time Series Anomaly Detection |
2025 |
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TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection |
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VLDB |
4.1905499e-05 |
| 4,082 |
Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series |
2023 |
VLDB |
6.4601453e-05 |
| 6,419 |
AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data |
2024 |
VLDB |
5.0621949e-05 |
| 7,183 |
TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms |
2022 |
VLDB |
4.8026282e-05 |
| 6,435 |
An Experimental Evaluation of Anomaly Detection in Time Series |
2024 |
VLDB |
5.0555305e-05 |
| 1,253 |
Anomaly Detection in Time Series: A Comprehensive Evaluation |
2022 |
VLDB |
0.00013019488 |