- Tsinghua-Peidan - AIOps course in Tsinghua.
- 基于机器学习的智能运维
- 搭建大规模高性能的时间序列大数据平台
- Yahoo大规模时列数据异常检测技术及其高性能可伸缩架构
- Netflix: Robust PCA
- LinkedIn: exponential smoothing
- Uber: multivariate non-linear model
- 智能运维|AIOps中的四大金刚都是谁？
- A Comparison of Mapping Approaches for Distributed Cloud Applications
- AIOps探索：基于VAE模型的周期性KPI异常检测方法
- Tools to Monitor and Visualize Microservices Architecture
- python-fp-growth，挖掘频繁项集
- Anomaly Detection with Twitter in R
- 百度开源时间序列打标工具：Curve
- Microsoft开源时间序列打标工具: TagAnomaly
- Anomaly Detection Examples
- facebook/prophet, Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
- google/CausalImpact, An R package for causal inference in time series
- 时间序列分析之ARIMA
- 时间序列特征提取库tsfresh
- Yahoo EGADS : A Java package to automatically detect anomalies in large scale time-series data
- Awesome Time Series Analysis and Data Mining
- Survey on Models and Techniques for Root-Cause Analysis
- 基于机器学习的智能运维
- HotSpot: Anomaly Localization for Additive KPIs With Multi-Dimensional Attributes
- Chinese：清华AIOps新作:蒙特卡洛树搜索定位多维指标异常
- Opprentice: Towards Practical and Automatic Anomaly Detection Through Machine Learning
- Robust and Rapid Clustering of KPIs for Large-Scale Anomaly Detection
- KPI-TSAD: A Time-Series Anomaly Detector for KPI Monitoring in Cloud Applications
- Anomaly Detection Based on Mining Six Local Data Features and BP Neural Network
- Generic and Robust Localization of Multi-Dimensional Root Causes
- Papers from Tsinghua NetMan Lab
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2018 AIOPS挑战赛预赛测试集 2018 AIOPS挑战赛预赛训练集
- 链接已经失效
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- 有API的延迟,并且有带标签!!!
- 智能运维前沿（清华裴丹团队的）
- AIOps智能运维（百度的）
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讲解的真的很不错,基础知识讲的通透.也有介绍基础算法和最新前沿的算法.
但不是专门介绍运维方向的.
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ARIMA
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ARIMA相关论文
[8] A. H. Yaacob, I. K. Tan, S. F. Chien, and H. K. Tan, “Arima based network anomaly detection,” in Communication Software and Networks,2010. ICCSN’10. Second International Conference on. IEEE, 2010, pp. 205–209.
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变式 SARIMA ,考虑季节性
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代码
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聚类方法- DBSCAN.
优点:抗干扰
缺点:不能探测到change radically.
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相关论文 [2019] Unsupervised Online Anomaly Detection with Parameter Adaptation for KPI Abrupt Changes
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- pandas可以读取xlsx、xls文件,csv文件,txt文件
- 返回类型是DataFrame
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整列往下移动一行
data['delay_1'] = data["delay"].shift(1)
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整组直接进行运算最快
DF['eee'] = DF['aaa'].values + DF['bbb'].values
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