The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965
Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
Powers, D. M. W. (2020) · arXiv · doi:10.48550/arxiv.2010.16061 · Cited by 5,440
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335388 · Cited by 5,207
A Dataset to Support Research in the Design of Secure Water Treatment Systems
Goh, J., Adepu, S., Junejo, K. N., & Mathur, A. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-71368-7_8 · Cited by 729 · cited 3×
“ys. For both of these datasets, we eliminated the rst 21;600 samples from the training data (normal data) since it took 5-6 hours to reach stabilization when the system was rst turned on according to [26]. In the anomaly detection process, we subdivide the original long multiple sequences into smaller time series by t…”
BackgroundMethodInfluentialSelf-citation
Cited by
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
Deng, A., & Hooi, B. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16523 · Cited by 1,413
Deep Learning for Anomaly Detection: A Survey
Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3130191 · Cited by 1,231
Machine Learning for Anomaly Detection: A Systematic Review
Nassif, A. B., Talib, M. A., Nasir, Q., & Dakalbab, F. M. (2021) · IEEE Access · doi:10.1109/access.2021.3083060 · Cited by 748
Himeur, Y., Ghanem, K., Alsalemi, A., Bensaali, F., & Amira, A. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116601 · Cited by 660
Metadata
- Authors
- Li, D., Chen, D., Jin, B., Shi, L., Goh, J., & Ng, S.-K.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 703-716
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2911200746
- Cited by
- 1,185
- References
- 5
- FWCI
- 35.46
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. https://app.cochat.ai/papers/10.1007/978-3-030-30490-4_56
- The paper
- Li, D., Chen, D., Jin, B., Shi, L., Goh, J., & Ng, S.-K. (2019). MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-30490-4_56
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
