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)
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Authors
- 1. Qinghua Liu (Ohio State University)
- 2. Paul Boniol (CNRS; INRIA; Paris Sciences et Lettres University; École Normale Supérieure)
- 3. Themis Palpanas (French University Institute; University of Paris)
- 4. John Paparrizos (Ohio State University)
BibTeX Citation
@article{liu_vldb24,
title = {{Time-Series Anomaly Detection: Overview and New Trends}},
author = {Liu, Qinghua and Boniol, Paul and Palpanas, Themis and Paparrizos, John},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4229--4232},
doi = {10.14778/3685800.3685842},
url = {https://doi.org/10.14778/3685800.3685842},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,256 | HYDRA: A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series | 2026 | SIGMOD | 5.093636e-05 |
| 10,271 | MUFASA: Fast and Accurate Multivariate Time-Series Clustering | 2026 | SIGMOD | 5.093636e-05 |
| 10,298 | The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis] | 2026 | SIGMOD | 5.093636e-05 |
| 10,747 | A Structured Study of Multivariate Time-Series Distance Measures | 2025 | SIGMOD | 5.093636e-05 |
| 10,796 | Understanding the Black Box: A Deep Empirical Dive into Shapley Value Approximations for Tabular Data | 2025 | SIGMOD | 5.093636e-05 |
| 10,961 | BURST: Rendering Clustering Techniques Suitable for Evolving Streams | 2025 | VLDB | 5.093636e-05 |
| 10,980 | Beyond Compression: A Comprehensive Evaluation of Lossless Floating-Point Compression | 2025 | VLDB | 5.093636e-05 |
| 11,056 | EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection | 2025 | VLDB | 5.093636e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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