Data augmentation using synthetic data for time series classification with deep residual networks

This is the companion repository for our paper titled "Data augmentation using synthetic data for time series classification with deep residual networks". This paper has been accepted for an oral presentation at the Workshop on Advanced Analytics and Learning on Temporal Data (AALTD) 2018 in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2018.

architecture resnet

Data

The data used in this project comes from the UCR archive, which contains the 85 univariate time series datasets we used in our experiements.

Code

The code is divided as follows:

Prerequisites

All python packages needed are listed in utils/pip-requirements.txt file and can be installed simply using the pip command for python3.6.

Results

The main contribution of a data augmentation technique is to improve the performance (accuracy) of a deep learning model especially for time series datasets with small training sets such as the DiatomSizeReduction (the smallest in the UCR archive) where we managed to increase the model's accuracy from 30% (without data augmentation) to 96% with data augmentation for a residual network architecture.

Meat DiatomSizeReduction
plot-meat-dataset plot-diatomsizereduction-dataset

Reference

If you re-use this work, please cite:

@InProceedings{IsmailFawaz2018,
  Title                    = {Data augmentation using synthetic data for time series classification with deep residual networks},
  Author                   = {Ismail Fawaz, Hassan and Forestier, Germain and Weber, Jonathan and Idoumghar, Lhassane and Muller, Pierre-Alain},
  Booktitle                = {International Workshop on Advanced Analytics and Learning on Temporal Data, {ECML} {PKDD}},
  Year                     = {2018}
}

Acknowledgement

We would like to thank NVIDIA Corporation for the Quadro P6000 grant and the M├ęsocentre of Strasbourg for providing access to the cluster.