This is a collection of time series data augmentation methods and an example use using Keras.

• 2020/04/16: Repository Created.
• 2020/06/22: Accepted to ICPR 2020 - B. K. Iwana and S. Uchida, Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teacher, ICPR 2020 LINK
• 2020/07/31: Survey Paper Posted on arXiv - B. K. Iwana and S. Uchida An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks, arXiv LINK

This code was developed in Python 3.5.2. and requires Tensorflow 1.10.0 and Keras 2.2.4

pip install keras==2.2.4 numpy==1.14.5 matplotlib==2.2.2 scikit-image==0.15.0 tqdm

cd docker
sudo docker build -t tsa .
docker run --runtime nvidia -rm -it -p 127.0.0.1:8888:8888 -v `pwd`:/work -w /work tsa jupyter notebook --allow-root

Newer docker installs might use --gpus all instead of --runtime nvidia

main.py was designed to use the UCR Time Series Archive 2018 datasets. To install the datasets, download the .zip file from https://www.cs.ucr.edu/~eamonn/time_series_data_2018/ and extract the contents into the data folder.

Augmentation description

Jupyter Notebook

Example: To train a 1D VGG on the FiftyWords dataset from the UCR Time Series Archive 2018 with 4x the training dataset in Jittering, use:

python3 main.py --gpus=0 --dataset=FiftyWords --preset_files --ucr2018 --normalize_input --train --save --jitter --augmentation_ratio=4 --model=vgg

B. K. Iwana and S. Uchida, "Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teacher," International Conference on Pattern Recognition, 2020.

@article{iwana2020empirical,
  title={An Empirical Survey of Data Augmentation for Time Series Classification
  with Neural Networks},
  author={Iwana, Brian Kenji and Uchida, Seiichi},
  journal={arXiv preprint arXiv:2007.15951},
  year={2020}
}