Code for the paper Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles
How to run the model:
- Execute cae_ensemble.py specifying the model parameters, for example:
python cae_ensemble.py --dataset 81 --diversity_factor 1 --beta_transfer 0.5 --rolling_size 16 --ensemble_members 20 --epochs 200 - The complete list of parameters is available at lines 963--1005 in cae_ensemble.py. The model parameters are the ones detailed in 1.
- The data sets number is related to the specification in lines 1064--1169.
- The structure for the data input is defined in data_provider.py.
- Results will be inserted in a database, calculations and connections are managed in metrics_insert.py.
Baselines:
- Use the same structure as the CAE-Ensemble model, just using their specific parameters.
If you use the code, please cite the following paper:
@article{pvldb/Ca22,
author = {David Campos and Tung Kieu and Chenjuan Guo and Feiteng Huang and Kai Zheng and
Bin Yang and Christian S. Jensen},
title = {{Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional
Ensembles}},
journal = {{PVLDB}},
volume = {15},
number = {3},
pages = {611--623},
year = {2022}
}