Hierarchical pattern matching for anomaly detection in time series
- Author
- Matthias Van Onsem (UGent) , Dieter De Paepe, Sander Vanden Hautte, Pieter Bonte (UGent) , Veerle Ledoux (UGent) , Annelies Lejon (UGent) , Femke Ongenae (UGent) , D. Dreesen and Sofie Van Hoecke (UGent)
- Organization
- Abstract
- As companies rely on an ever increasing number of connected devices for their day to day operations, a need arises for automated anomaly detectors to constantly observe crucial device metrics in real time to prevent downtime and data loss. As production environments tend to monitor a huge amount of these metrics, it prevents current state-of-the-art techniques to be deployed as the required computational resources is too high. This paper proposes a lightweight anomaly detection method that can be deployed in these environments without a reduction in accuracy. The approach works fully online, and does not require an extensive history set to be kept in memory. The method is benchmarked on the publicly available Numenta dataset, as well as a network monitoring dataset from different environments provided by a network management solution vendor. These benchmarks show the proposed technique to be very competitive with the current state-of-the-art and exceeding it in production applicability.
- Keywords
- Anomaly detection, Network monitoring, Time series
Downloads
-
DS560.pdf
- full text (Published version)
- |
- open access
- |
- |
- 693.45 KB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8770497
- MLA
- Van Onsem, Matthias, et al. “Hierarchical Pattern Matching for Anomaly Detection in Time Series.” COMPUTER COMMUNICATIONS, vol. 193, 2022, pp. 75–81, doi:10.1016/j.comcom.2022.06.027.
- APA
- Van Onsem, M., De Paepe, D., Vanden Hautte, S., Bonte, P., Ledoux, V., Lejon, A., … Van Hoecke, S. (2022). Hierarchical pattern matching for anomaly detection in time series. COMPUTER COMMUNICATIONS, 193, 75–81. https://doi.org/10.1016/j.comcom.2022.06.027
- Chicago author-date
- Van Onsem, Matthias, Dieter De Paepe, Sander Vanden Hautte, Pieter Bonte, Veerle Ledoux, Annelies Lejon, Femke Ongenae, D. Dreesen, and Sofie Van Hoecke. 2022. “Hierarchical Pattern Matching for Anomaly Detection in Time Series.” COMPUTER COMMUNICATIONS 193: 75–81. https://doi.org/10.1016/j.comcom.2022.06.027.
- Chicago author-date (all authors)
- Van Onsem, Matthias, Dieter De Paepe, Sander Vanden Hautte, Pieter Bonte, Veerle Ledoux, Annelies Lejon, Femke Ongenae, D. Dreesen, and Sofie Van Hoecke. 2022. “Hierarchical Pattern Matching for Anomaly Detection in Time Series.” COMPUTER COMMUNICATIONS 193: 75–81. doi:10.1016/j.comcom.2022.06.027.
- Vancouver
- 1.Van Onsem M, De Paepe D, Vanden Hautte S, Bonte P, Ledoux V, Lejon A, et al. Hierarchical pattern matching for anomaly detection in time series. COMPUTER COMMUNICATIONS. 2022;193:75–81.
- IEEE
- [1]M. Van Onsem et al., “Hierarchical pattern matching for anomaly detection in time series,” COMPUTER COMMUNICATIONS, vol. 193, pp. 75–81, 2022.
@article{8770497,
abstract = {{As companies rely on an ever increasing number of connected devices for their day to day operations, a need arises for automated anomaly detectors to constantly observe crucial device metrics in real time to prevent downtime and data loss. As production environments tend to monitor a huge amount of these metrics, it prevents current state-of-the-art techniques to be deployed as the required computational resources is too high. This paper proposes a lightweight anomaly detection method that can be deployed in these environments without a reduction in accuracy. The approach works fully online, and does not require an extensive history set to be kept in memory. The method is benchmarked on the publicly available Numenta dataset, as well as a network monitoring dataset from different environments provided by a network management solution vendor. These benchmarks show the proposed technique to be very competitive with the current state-of-the-art and exceeding it in production applicability.}},
author = {{Van Onsem, Matthias and De Paepe, Dieter and Vanden Hautte, Sander and Bonte, Pieter and Ledoux, Veerle and Lejon, Annelies and Ongenae, Femke and Dreesen, D. and Van Hoecke, Sofie}},
issn = {{0140-3664}},
journal = {{COMPUTER COMMUNICATIONS}},
keywords = {{Anomaly detection,Network monitoring,Time series}},
language = {{eng}},
pages = {{75--81}},
title = {{Hierarchical pattern matching for anomaly detection in time series}},
url = {{http://doi.org/10.1016/j.comcom.2022.06.027}},
volume = {{193}},
year = {{2022}},
}
- Altmetric
- View in Altmetric
- Web of Science
- Times cited: