Awesome
Code for the paper MetaAD: A Prototype-oriented Meta Anomaly Detection Framework for Multivariate Time Series
To run with local environment
Install dependencies using python 3.8+ :
pip install -r requirements.txt
Run the model:
python runner10.py --test_train_step2 10
test_train_step2 is the number of data used for meta-training.
Data
SMD：
Download the processed data form Google cloud to './SMD/'. SMD Data。
MSL：
Run the data process code in the './MSL/'.
python ./MSL/data_press_1.py