Special Issue on Unsupervised Anomaly Detection
Source abstract
Abstract
Anomaly detection (also known as outlier detection) is the task of finding instances in a dataset which deviate markedly from the norm [...]
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short
Research context
OpenAlex topics: Anomaly Detection Techniques and Applications · Time Series Analysis and Forecasting · Network Security and Intrusion Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, Ulm University of Applied Sciences, 89075 Ulm, Germany
References (12)
Show source-provided references
1. Herskind Sejr, J., Christiansen, T., Dvinge, N., Hougesen, D., Schneider-Kamp, P., and Zimek, A. (2021). Outlier Detection with Explanations on Music Streaming Data: A Case Study with Danmark Music Group Ltd. Appl. Sci., 11.10.3390/app11052270 ↗
2. Qin, F., Feng, H., Yang, T., and Hu, B. (2021). Low-Cost Active Anomaly Detection with Switching Latency. Appl. Sci., 11.10.3390/app11072976 ↗
3. Shang, Z., Zhang, Y., Zhang, X., Zhao, Y., Cao, Z., and Wang, X. (2021). Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM. Appl. Sci., 11.10.3390/app112311353 ↗
4. Jiang, J.R., Kao, J.B., and Li, Y.L. (2021). Semi-Supervised Time Series Anomaly Detection Based on Statistics and Deep Learning. Appl. Sci., 11.10.3390/app11156698 ↗
5. Al-amri, R., Murugesan, R.K., Man, M., Abdulateef, A.F., Al-Sharafi, M.A., and Alkahtani, A.A. (2021). A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data. Appl. Sci., 11.10.3390/app11125320 ↗
6. Cheng, C.S., Chen, P.W., and Wu, Y.T. (2023). Phase I Analysis of Nonlinear Profiles Using Anomaly Detection Techniques. Appl. Sci., 13.10.3390/app13042147 ↗
7. Lian, Y., Geng, Y., and Tian, T. (2023). Anomaly Detection Method for Multivariate Time Series Data of Oil and Gas Stations Based on Digital Twin and MTAD-GAN. Appl. Sci., 13.10.3390/app13031891 ↗
8. Rollón de Pinedo, Á., Couplet, M., Iooss, B., Marie, N., Marrel, A., Merle, E., and Sueur, R. (2021). Functional Outlier Detection by Means of h-Mode Depth and Dynamic Time Warping. Appl. Sci., 11.10.3390/app112311475 ↗
9. Jasra, S.K., Valentino, G., Muscat, A., and Camilleri, R. (2022). Hybrid Machine Learning—Statistical Method for Anomaly Detection in Flight Data. Appl. Sci., 12.10.3390/app122010261 ↗
10. Kozitsin, V., Katser, I., and Lakontsev, D. (2021). Online Forecasting and Anomaly Detection Based on the ARIMA Model. Appl. Sci., 11.10.3390/app11073194 ↗
11. Rewicki, F., Denzler, J., and Niebling, J. (2023). Is It Worth It? Comparing Six Deep and Classical Methods for Unsupervised Anomaly Detection in Time Series. Appl. Sci., 13.10.3390/app13031778 ↗
12. Pham, T.A., Lee, J.H., and Park, C.S. (2022). MST-VAE: Multi-Scale Temporal Variational Autoencoder for Anomaly Detection in Multivariate Time Series. Appl. Sci., 12.10.3390/app121910078 ↗