论文标题
集合2：通过EVT汇合框架在通信网络中进行季节性KPI的异常检测
Ensemble2: Anomaly Detection via EVT-Ensemble Framework for Seasonal KPIs in Communication Network
论文作者
论文摘要
KPI异常检测是网络管理系统的重要功能。传统方法要么需要先验知识或手动设置阈值。为了克服这些缺点，我们提出了集成框架，该框架应用集合学习以提高外源能力。同时，根据极值理论自动调整阈值。该模型在生产数据集上进行了测试，以验证其有效性。我们使用在线学习进一步优化了该模型，最后在英特尔i5平台上以〜10 pts/s的速度运行。
KPI anomaly detection is one important function of network management system. Traditional methods either require prior knowledge or manually set thresholds. To overcome these shortcomings, we propose the Ensemble2 framework, which applies ensemble learning to improve exogenous capabilities. Meanwhile, automatically adjusts thresholds based on extreme value theory. The model is tested on production datasets to verify its effectiveness. We further optimize the model using online learning, and finally running at a speed of ~10 pts/s on an Intel i5 platform.
