Python Implementation of RObustTraining forTime-Series (RO-TS) for the paper: "Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis" by Taha Belkhouja, Yan Yan, and Janardhan Rao Doppa.
pip install -r requirement.txt
By default, data is stored in experim_path_{dataset_name}
. Directory can be changed in RO_TS.py
- The dataset can be obtained as .zip file from "The UCR Time Series Classification Repository".
- Download the .zip file and extract it it in
UCRDatasets/{dataset_name}
directory. - Run the following command for pre-processing a given dataset while specifying if it is multivariate, for example, on SyntheticControl dataset
python preprocess_dataset.py --dataset_name=SyntheticControl --multivariate=False
The results will be stored in Dataset
directory.
- Example training run
python RO_TS.py --dataset_name=SyntheticControl --K=10 --rots_beta=5e-1 --rots_lambda=1e-2 --batch=11
- Example testing run
python test_RO_TS_model.py --dataset_name=SyntheticControl --rots_beta=5e-1 --rots_lambda=1e-2