The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model
Abstract
In this work, we propose a VAE-LSTM hybrid model as an unsupervised approach for anomaly detection in time series. Our model utilizes both a VAE module for forming robust local features over short windows and a LSTM module for estimating the long term correlation in the series on top of the features inferred from the VAE module. As a result, our detection algorithm is capable of identifying anomalies that span over multiple time scales. We demonstrate the effectiveness of our detection algorithm on five real world problems and find our method outperforms three other commonly used detection methods.
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Cites
Auto-Encoding Variational Bayes
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Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
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Long Short Term Memory Networks for Anomaly Detection in Time Series.
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A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data
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Unsupervised real-time anomaly detection for streaming data
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Deepvo: towards end-to-end visual odometry with deep recurrent convolutional neural networks
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“Hybrid : Hybrid models are a common approach for video analysis where a representation learning module is used to distil spatial information in a single image frame and a sequential module is applied to model the temporal correlation across a series of frames [13, 14].”
BackgroundSelf-citationUnsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
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Li, G., & Jung, J. J. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.10.008 · Cited by 361
LSTM-Autoencoder-Based Anomaly Detection for Indoor Air Quality Time-Series Data
Wei, Y., Jang-Jaccard, J., Xu, W., Sabrina, F., Camtepe, S., & Boulic, M. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3230361 · Cited by 269
ADT: Time series anomaly detection for cyber-physical systems via deep reinforcement learning
Yang, X., Howley, E., & Schukat, M. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103825 · Cited by 47
NODLINK: An Online System for Fine-Grained APT Attack Detection and Investigation
Li, S., Dong, F., Xiao, X., Wang, H., Shao, F., Chen, J., Guo, Y., Chen, X., & Li, D. (2024) · Proceedings 2024 Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.23204 · Cited by 45
Kotsiopoulos, T., Papakostas, G., Vafeiadis, T., Dimitriadis, V., Nizamis, A., Bolzoni, A., Bellinati, D., Ioannidis, D., Votis, K., Tzovaras, D., et al. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124839 · Cited by 19
Metadata
- Authors
- Lin, S., Clark, R., Birke, R., Schonborn, S., Trigoni, N., & Roberts, S.
- Venue
- ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2020-05
- Pages
- 4322-4326
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W3015959599
- Cited by
- 312
- References
- 17
- FWCI
- 10.48
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Lin, S., Clark, R., Birke, R., Schonborn, S., Trigoni, N., & Roberts, S. (2020). Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp40776.2020.9053558
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