TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection
Summary: TSB-UAD is a reproducible benchmark for univariate time-series anomaly detection, aggregating 13,766 labeled series across diverse domains and anomaly characteristics. It combines 18 existing datasets with principled and transformed synthetic collections, enabling robust cross-dataset evaluation and leaderboards. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. John Paparrizos (University of Chicago)
- 2. Yuhao Kang (University of Chicago)
- 3. Paul Boniol (Université de Paris)
- 4. Ruey S. Tsay (University of Chicago)
- 5. Themis Palpanas (Institut universitaire de France; Université de Paris)
- 6. Michael J. Franklin (University of Chicago)
BibTeX Citation
@article{paparrizos_vldb22,
title = {{TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection}},
author = {Paparrizos, John and Kang, Yuhao and Boniol, Paul and Tsay, Ruey S. and Palpanas, Themis and Franklin, Michael J.},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {8},
pages = {1697--1711},
doi = {10.14778/3529337.3529354},
url = {https://doi.org/10.14778/3529337.3529354},
year = {2022}
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