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AIOps (Artificial Intelligence for IT Operations), that is, intelligent operation and maintenance, is to combine the capabilities of artificial intelligence with operation and maintenance, and improve the efficiency of operation and maintenance through machine learning methods.
The author is fortunate to join the world's leading communication equipment manufacturer after graduating from university, and has in-depth cooperation with Chinese operators. As the main core designer, he designed multiple sets of AIOPS systems for the operator's large-scale data center, and shared the previously collected data. , I hope to do my best to promote the development of the community.
Last update date: 2022/1
The author has set up an AIOPS technical exchange group with friends all over Silicon Valley, Singapore, Tencent, Ali, Zhejiang University, etc. Like-minded friends are welcome to contact me to join! 
Algorithm Architecture: https://zhuanlan.zhihu.com/p/466955597?
Engineering Architecture: https://zhuanlan.zhihu.com/p/511095084
1. 《Clustering Interval-Censored Time-Series for Disease Phenotyping》
2. Correlative Channel-Aware Fusion for Multi-View Time Series Classification
3. Learnable Dynamic Temporal Pooling for Time Series Classification
4. ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification
5. Joint-Label Learning by Dual Augmentation for Time Series Classification
6. Learning Representations for Incomplete Time Series Clustering
7. Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Time Intervals
8. Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
9.《Clustering Intrusion Detection Alarms to Support Root Cause Analysis》
10.《Robust and Rapid Clustering of KPIs for Large-Scale Anomaly Detection》
11.《A Density-Based Algorithm for Discovering Clusters》
12.《YADING: Fast Clustering of Large-Scale Time Series Data》
13.《k-Shape: Efficient and Accurate Clustering of Time Series》
14.《Probabilistic Alert Correlation》
15. https://mp.weixin.qq.com/s/AjE7uP7ApVPyL_HdQDkk5g
1. Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
2. Time Series Anomaly Detection with Multiresolution Ensemble Decoding
3. Outlier Impact Characterization for Time Series Data
4. F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams
5. Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network
6.《Time-Series Anomaly Detection Service at Microsof》
7.《Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications》
8.《Anomaly Detection in Streams with Extreme Value Theory》
9. https://github.com/yzhao062/anomaly-detection-resources
13. https://zr9558.com/2018/11/30/timeseriespredictionfbprophet/
10. https://zhuanlan.zhihu.com/p/150316014
11. https://www.zhihu.com/question/29507442/answer/1212624591
12. https://www.zhihu.com/question/283641524/answer/1211966582
15. https://zhuanlan.zhihu.com/p/108843309
16. https://keras.io/examples/timeseries/timeseries_transformer_classification/
17. https://blog.csdn.net/Liao_Wenzhe/article/details/119796325
18. https://blog.csdn.net/Liao_Wenzhe/article/details/119732579
19.《Robust Random Cut Forest Based Anomaly Detection On Streams》
20. https://mp.weixin.qq.com/s?__biz=MzA5NTQ5MzE5OQ%3D%3D&chksm=8b685c6cbc1fd57a27a476fe1f15a59b25f4928094b2a49b7e8f054ed0cc5dc29a243173bf2e&idx=1&mid=2653057356&scene=21&sn=85d82226c7f66685ec8cf486569976dc#wechat_redirect
1.《HotSpot: Anomaly Localization for Additive KPIs With Multi-Dimensional Attributes》
2.《CoFlux: Robustly Correlating KPIs by Fluctuations for Service Troubleshooting》
3.《Correlating Events with Time Series for Incident Diagnosis》
4.《Detecting Leaders from Correlated Time Series》
5. Deep reconstruction of strange attractors from time series
6. High-recall causal discovery for autocorrelated time series with latent confounders
7. Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso
8. Normalizing Kalman Filters for Multivariate Time Series Analysis
1.《Clustering Intrusion Detection Alarms to Support Root Cause Analysis》
2.《CMining Alarm Clusters to Improve Alarm Handling Efficiency》
3.《Automatically and Adaptively Identifying Severe Alerts for Online Service Systems》
4.《Understanding and Handling Alert Storm for Online Service Systems》
5.《CoFlux: Robustly Correlating KPIs by Fluctuations for Service Troubleshooting》
6.《Aggregation and Correlation of Intrusion-Detection Alerts》
7.《Generic and Robust Localization of Multi-Dimensional Root Causes》
8.《Probabilistic Alert Correlation》
1.《Clustering Intrusion Detection Alarms to Support Root Cause Analysis》
2.《FluxRank: A Widely-Deployable Framework to Automatically Localizing Root Cause Machines for Software Service Failure Mitigation》
3.《Adtributor: Revenue debugging in advertising systems》
4.《HotSpot: Anomaly Localization for Additive KPIs With Multi-Dimensional Attributes》
1.《Dynamic Nonlinear Matrix Completion for Time-Varying Data Imputation》
2. Generative Semi-Supervised Learning for Multivariate Time Series Imputation
1. Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting
2. Dynamic Gaussian Mixture Based Deep Generative Model for Robust Forecasting on Sparse Multivariate Time Series
3. Temporal Latent Autoencoder: A Method for Probabilistic Multivariate Time Series Forecasting
4. Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting
5. Time-Series Event Prediction with Evolutionary State Graph
6. Probabilistic Time Series Forecasting with Shape and Temporal Diversity
7. Benchmarking Deep Learning Interpretability in Time Series Predictions
8. Adversarial Sparse Transformer for Time Series Forecasting
9. Deep Rao-Blackwellised Particle Filters for Time Series Forecasting
1. Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
2. Time Series Domain Adaptation via Sparse Associative Structure Alignment
3. Transformer-Style Relational Reasoning with Dynamic Memory Updating for Temporal Network Modeling
1. Bridging Towers of Multi-Task Learning with a Gating Mechanism for Aspect-Based Sentiment Analysis and Sequential Metaphor Identification
2. C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer
3. Inductive Graph Neural Networks for Spatiotemporal Kriging
4. Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance
5. Continuous-Time Attention for Sequential Learning
6. Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks
7. ARMA Nets: Expanding Receptive Field for Dense Prediction
8. Learning Long-Term Dependencies in Irregularly-Sampled Time Series
9. Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks
10. Learnable Group Transform For Time-Series
1. ChronoR: Rotation Based Temporal Knowledge Graph Embedding
2. Learning from History: Modeling Temporal Knowledge Graphs with Sequential CopyGeneration Networks
3. Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs
4. CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations
5. ARMA Nets: Expanding Receptive Field for Dense Prediction
6. Unsupervised Transfer Learning for Spatiotemporal Predictive Networks
1. Set Functions for Time Series
2. Learning from Irregularly-Sampled Time Series: A Missing Data Perspective
1. "Forecasting at Scale"
2. "Box-Cox Transformation for Simple Linear Regression"
3. https://github.com/NetManAIOps
4. https://github.com/AI-Sphere/Awesome-Noah
5. https://github.com/Tencent/Metis
6. https://github.com/linjinjin123/awesome-AIOps
7. WeChat Official Account: Frontier of Intelligent Operation and Maintenance
8. WeChat public account: Timing people
9. https://blog.csdn.net/Liao_Wenzhe/article/details/119579709
10. https://blog.csdn.net/Liao_Wenzhe/article/details/11933514