Self-Supervised Learning for Online Anomaly Detection in High-Dimensional Data Streams
In this paper, we address the problem of detecting and learning anomalies in high-dimensional data-streams in real-time. Following a data-driven approach, we propose an online and multivariate anomaly detection method that is suitable for the timely and accurate detection of anomalies. We propose ou...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI
2023
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| Subjects: | |
| Online Access: | View Fulltext in Publisher View in Scopus |