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时间序列异常检测
2022-07-26 09:53:00 【条件漫步】
@创建于：2022.07.25
@修改于：2022.07.25
1、方法
（1）业务判读、统计（3 σ \sigma σ，箱线图）
（2）看成聚类问题（kNN，kMeans）
（3）看成预测问题
时间序列预测
（4）时序分解后，统计残差或拟合残差
（5）Isolation Forest
（6）滑窗法（左右滑窗，扩张滑窗）与变异系数的组合
（7）深度学习方法
2、工具
3、价值连接
时间序列异常检测（一）—— 算法综述
时间序列异常检测（二）—— 基于KDD99数据集的实战
时间序列异常检测（三）—— 告警策略与根因定位
时间序列异常检测（四）—— 根因定位落地实践
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