The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges
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Cites
Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001) · Physical Review E · doi:10.1103/physreve.64.061907 · Cited by 3,032
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., & Pei, D. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330672 · Cited by 1,804
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
Hundman, K., Constantinou, V., Laporte, C., Colwell, I., & Soderstrom, T. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3219845 · Cited by 1,771
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
Dau, H. A., Bagnall, A., Kamgar, K., Yeh, C.-C. M., Zhu, Y., Gharghabi, S., Ratanamahatana, C. A., & Keogh, E. (2019) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2019.1911747 · Cited by 1,128
Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics
Lee, S. S., & Mykland, P. A. (2008) · Review of Financial Studies · doi:10.1093/rfs/hhm056 · Cited by 862
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series
Munir, M., Siddiqui, S. A., Dengel, A., & Ahmed, S. (2019) · IEEE Access · doi:10.1109/access.2018.2886457 · Cited by 696
Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT
Chen, Z., Chen, D., Zhang, X., Yuan, Z., & Cheng, X. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3100509 · Cited by 595
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines
Choi, K., Yi, J., Park, C., & Yoon, S. (2021) · IEEE Access · doi:10.1109/access.2021.3107975 · Cited by 588
2015 IEEE International Conference on Data Mining
(2015) · doi:10.1109/icdm36327.2015 · Cited by 521
Cited by
Noorchenarboo, M., & Grolinger, K. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2024.115177 · Cited by 38
Temporal subgraph contrastive learning for anomaly detection on dynamic attributed graphs
Yu, Y., Li, X., Shao, M., Sun, Y., & Wang, W. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06402-8 · Cited by 2
Metadata
- Authors
- Li, G., & Jung, J. J.
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 91
- Pages
- 93-102
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W4306645406
- Cited by
- 361
- References
- 68
- FWCI
- 22.86
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges. https://app.cochat.ai/papers/10.1016/j.inffus.2022.10.008
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- Li, G., & Jung, J. J. (2023). Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges. Information Fusion. https://doi.org/10.1016/j.inffus.2022.10.008
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.
