Research on Anomaly Detection in Time-Series Streaming Data
Luyang Gong · 2023
The detection of anomalies in streaming data is crucial in enterprise operations, employing statistical and machine learning methods to identify irregularities. This enhances service stability and reduces operational complexity. However, existing monitoring platforms face challenges such as complex rule configurations and a lack of unified standards. To address these issues, this paper proposes a container performance anomaly detection module that combines statistical and machine learning approaches. This module, through steps including streaming data collection, data preprocessing, anomaly detection models, and monitoring alerts, achieves rapid detection and accurate identification of container performance anomalies. In comparison to traditional monitoring platforms, this module not only saves manpower but also demonstrates higher accuracy.