A Semi-Supervised VAE Based Active Anomaly Detection Framework in Multivariate Time Series for Online Systems
The structure of SLA-VAE is presented as follows. We provide a semi-supervised VAE based active anomaly detection framework in multivariate time series for online systems. It consists of three components, namely anomaly feature extraction, semi-supervised variational auto-encoder based anomaly detection, and active learning based on model uncertainty.
- The anomaly definition based on feature extraction module can be available to all multi-variate time series and represent the anomalies in the same degree.
- Based on extracted features, we propose semi-supervised VAE to identify anomalies to avoid the risk of susceptibility to anomalous input introduced by unsupervised VAE.
- Then we adopt the active learning and put forward an active anomaly detection framework so that we can learn and update the online detection model using a small amount of highly uncertain samples.
Please refer to the two testCases to run the code.
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├── data """Cold Start Data [model parameters + samples]"""
│ ├── param
│ │ ├── multiple_kpi
│ │ │ └── multi_kpi_ad_param.pkl
│ │ └── single_kpi
│ │ └── kpi_ad_param.pkl
│ ├── sample
│ │ ├── multiple_kpi
│ │ │ └── sample_sets.txt
│ │ └── single_kpi
│ │ └── sample_sets.txt
│ └── sample
│ └── multi_ad.png
├── src """Source Code"""
│ ├── common
│ │ ├── constant.py
│ │ ├── exception.py
│ │ ├── util_thre_rec.py
│ │ ├── utils_cleaning.py
│ │ ├── utils_dataframe.py
│ │ ├── utils_smooth.py
│ │ ├── utils_time_series.py
│ │ ├── utils_timestamp.py
│ │ └── utils_window.py
│ ├── feature
│ │ └── feature_ext.py
│ ├── model
│ │ ├── kde.py
│ │ ├── pid.py
│ │ └── semi_vae.py
│ └── service
│ ├── kpi_ad.py
│ └── multi_kpi_ad.py
└── tests """TestCases"""
├── feature
│ └── test_feature_ext.py
├── model
│ └── test_semi_vae.py
└── service
├── test_kpi_ad.py
└── test_multi_kpi_ad.py
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