MCAD: Multivariate Correlation Anomaly Data Generator
Summary: MCAD synthesizes multivariate time series with configurable cross-channel correlation patterns and coordinated, non-recurring anomalies. MCAD-VIZ exposes how black-box detectors fail on these richer anomaly types, motivating correlation-aware benchmarks. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Christos C. Papadopoulos (Aalborg University; Athena Research Center; National and Kapodistrian University of Athens)
- 2. Alkis Simitsis (Athena Research Center)
- 3. Torben Bach Pedersen (Aalborg University)
BibTeX Citation
@article{papadopoulos_vldb26,
title = {{MCAD: Multivariate Correlation Anomaly Data Generator}},
author = {Papadopoulos, Christos C. and Simitsis, Alkis and Pedersen, Torben Bach},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4638--4641},
doi = {10.14778/3827998.3828085},
url = {https://doi.org/10.14778/3827998.3828085},
year = {2026}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,629 | SAND: Streaming Subsequence Anomaly Detection | 2021 | VLDB | 0.00010036401 |
| 5,115 | TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms | 2022 | VLDB | 6.2679451e-05 |
| 7,641 | A Structured Study of Multivariate Time-Series Distance Measures | 2025 | SIGMOD | 5.4772833e-05 |
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