Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection

arXiv:2311.16191v2 Announce Type: replace
Abstract: Anomaly detection significantly enhances the robustness of cloud systems. While neural network-based methods have recently demonstrated strong advantages, they encounter practical challenges in cloud environments: the contradiction between the impracticality of maintaining a unique model for each service and the limited ability to deal with diverse normal patterns by a unified model, as well as issues with handling heavy traffic in real time and short-term anomaly detection sensitivity.
Thus, we propose MACE, a multi-normal-pattern accommodated …

abstract advantages anomaly anomaly detection arxiv challenges cloud cloud environments cs.ai cs.lg detection domain environments network neural network practical robustness series service systems time series type