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)
Incoming Non-self Citations Over Time
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}
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