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An Experimental Evaluation of Anomaly Detection in Time Series
Summary: Provides a taxonomy of time-series anomaly detectors and a unified empirical comparison of 17 state-of-the-art algorithms on real and synthetic benchmarks using both point and range metrics. Thoroughly evaluates effectiveness, efficiency, and robustness across anomaly rates, data size, dimensionality, anomaly patterns, and threshold settings, and offers practical guidance for method selection.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13725
- Venue
- VLDB
- Year
- 2024
- Pagerank
- 5.0603878e-05
- Overall Rank
- 6,440 | 55.20%
- DOI
-
10.14778/3620393.3632110
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 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 |
0.00013032074 |
| 1,516 |
k-Shape: Efficient and Accurate Clustering of Time Series |
2015 |
SIGMOD |
0.00011586255 |
| 1,634 |
Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series |
2021 |
VLDB |
0.00011058945 |
| 1,854 |
Distance-based Outlier Detection in Data Streams |
2016 |
VLDB |
0.00010317762 |
| 1,921 |
Apache IoTDB: Time-series Database for Internet of Things |
2020 |
VLDB |
0.00010082827 |
| 2,029 |
SAND: Streaming Subsequence Anomaly Detection |
2021 |
VLDB |
9.740868e-05 |
| 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 |
| 3,012 |
NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing |
2019 |
VLDB |
7.7153586e-05 |
| 3,943 |
Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection |
2022 |
VLDB |
6.6099833e-05 |
| 8,083 |
A New Distributional Treatment for Time Series and An Anomaly Detection Investigation |
2022 |
VLDB |
4.5903492e-05 |
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5.0670573e-05 |
| 4,079 |
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6.4663636e-05 |
| 11,094 |
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| 1,253 |
Anomaly Detection in Time Series: A Comprehensive Evaluation |
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