Official implementation of AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data
by: Mohamed Ragab*, Emadeldeen Eldele*, Wee Ling Tan, Chuan-Sheng Foo, Zhenghua Chen, Min Wu, Chee Kwoh, Xiaoli Li
AdaTime is a PyTorch suite to systematically and fairly evaluate different domain adaptation methods on time series data.
Requirmenets:
- Python3
- Pytorch==1.7
- Numpy==1.20.1
- scikit-learn==0.24.1
- Pandas==1.2.4
- skorch==0.10.0 (For DEV risk calculations)
- openpyxl==3.0.7 (for classification reports)
- Wandb=0.12.7 (for sweeps)
Datasets
Available Datasets
We used four public datasets in this study. We also provide the preprocessed versions as follows:
Adding New Dataset
Structure of data
To add new dataset (e.g., NewData), it should be placed in a folder named: NewData in the datasets directory.
Since “NewData” has several domains, each domain should be split into train/test splits with naming style as
“train_x.pt” and “test_x.pt”.
The structure of data files should in dictionary form as follows: