VRGAN: A Deep Generative Framework for Unsupervised Anomaly Detection in Multivariate Time-Series Data
- DOI
- 10.3390/app16199556
- Published
- 2026-09-25
- Container
- Applied Sciences
- Publisher
- MDPI AG
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.3390/app16199556,
title = {VRGAN: A Deep Generative Framework for Unsupervised Anomaly Detection in Multivariate Time-Series Data},
author = {Joung Min Choi and Connor L. Brown and Amy Pruden and Liqing Zhang},
year = {2026},
journal = {Applied Sciences},
doi = {10.3390/app16199556},
url = {https://doi.org/10.3390/app16199556}
}RIS
TY - JOUR TI - VRGAN: A Deep Generative Framework for Unsupervised Anomaly Detection in Multivariate Time-Series Data AU - Joung Min Choi AU - Connor L. Brown AU - Amy Pruden AU - Liqing Zhang PY - 2026 JO - Applied Sciences DO - 10.3390/app16199556 UR - https://doi.org/10.3390/app16199556 ER -
APA
Choi, J. M., Brown, C. L., Pruden, A., & Zhang, L. (2026). VRGAN: A Deep Generative Framework for Unsupervised Anomaly Detection in Multivariate Time-Series Data. Applied Sciences. https://doi.org/10.3390/app16199556
Source records
- crossref · retrieved 2026-11-10T09:02:04.999Z