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Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection. (arXiv:2201.10323v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
Key Performance Indicators (KPI), which are essentially time series data,
have been widely used to indicate the performance of telecom networks. Based on
the given KPIs, a large set of anomaly detection algorithms have been deployed
for detecting the unexpected network incidents. Generally, unsupervised anomaly
detection algorithms gain more popularity than the supervised ones, due to the
fact that labeling KPIs is extremely time- and resource-consuming, and
error-prone. However, those unsupervised anomaly detection algorithms often
suffer from excessive false alarms, …
active learning anomaly detection arxiv big detection learning time time series unsupervised