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
- 2021/05/11: Tensorflow v1 branched. The master will now support Tensorflow v2.
- 2021/07/15: Survey Paper Published on PLOS ONE - B. K. Iwana and S. Uchida An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks, PLOS ONE 16(7): e0254841, LINK
This code was developed in Python 3.6.9. and requires Tensorflow 2.4.1 and Keras 2.2.4
pip install tensorflow-gpu==2.4.1 keras==2.2.4 numpy==1.19.5 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.
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=CBF --preset_files --ucr2018 --normalize_input --train --save --jitter --augmentation_ratio=4 --model=vgg
B. K. Iwana and S. Uchida, "An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks," arXiv, 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}
}