Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection
Summary: New accuracy measures for time-series anomaly detection that address range-based anomalies. Extends AUC-based evaluation and introduces VUS (Volume Under the Surface), a parameter-free, threshold-independent family; claims superior robustness to noise, misalignment, and varying anomaly cardinality. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. John Paparrizos (Ohio State University)
- 2. Paul Boniol (Université Paris Cité)
- 3. Themis Palpanas (Institut Universitaire de France; Université Paris Cité)
- 4. Ruey S. Tsay (University of Chicago)
- 5. Aaron Elmore (University of Chicago)
- 6. Michael J. Franklin (University of Chicago)
BibTeX Citation
@article{paparrizos_vldb22,
title = {{Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection}},
author = {Paparrizos, John and Boniol, Paul and Palpanas, Themis and Tsay, Ruey S. and Elmore, Aaron and Franklin, Michael J.},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {11},
pages = {2774--2787},
doi = {10.14778/3551793.3551830},
url = {https://doi.org/10.14778/3551793.3551830},
year = {2022}
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