README.md

Enhancing Time Series Classification with Self-Supervised Learning

This is the code of our paper "Enhancing Time Series Classification with Self-Supervised Learning" accepted as short paper at 2023 15th International Conference on Agents and Artificial Interlligence.
This work was done by Ali Ismail-Fawaz, Maxime Devanne, Jonathan Weber and Germain Forestier.

Triplet Loss Mechanism

The Triplet Loss used in this work was proposed by F. Schroff et al..

Triplet Generation

Positive Samples

Negative Samples

Dataset

This model has been tested on the 85 univariate time series datasets of the UCRArchive

Usage of code

To run the code of the TRILITE model on a dataset of the UCRArchive, simply run python3 main.py -e fcn -d Coffee

To run the code on the TRILITE model in a semi-supervised way on a dataset of the UCRArchive,
simply run python3 main_semi_supervised.py -e fcn -d Coffee -p 30, -p is to specify the percentage of the semi-supervised split.

To apply classication on the results, simply run python3 apply_classifier -e fcn -o results/ -d Coffee or
python3 apply_classifier -e fcn -o results_semi_30/ -d Coffee to apply classification on the semi-supervised split results.

To avoid running each time on one dataset at the time, simply use the bash files run_ucr.sh, run_semi_ucr.sh and run_classifier_ucr.sh for each case.

Adaptation of Code

The change that should be done is the directory in which the datasets are stored. The variable to be changed is this line folder_path.

Results

Results of the conducted experiments can be found in the two csv files, one for the enhancement of FCN and one for the Semi-Supervised setup.

TRILITE with a Fully Connected Layer Classifier

TRILITE+FCN with a Fully Connected Layer Classifier (+ means concatenation of features)

TRILITE+FCN in a Semi-Supervised environment with a RIDGE Classifier

Latent Space Representation in 2D using TRILITE on the SyntheticControl Dataset of the UCR archive

Reference

If you use this code, please cite our paper:

@inproceedings{ismail-fawaz2022trilite,
  author = {Ismail-Fawaz, Ali and Devanne, Maxime and Weber, Jonathan and Forestier, Germain},
  title = {Enhancing Time Series Classification with Self-Supervised Learning},
  booktitle = {15th International Conference on Agents and Artificial Intelligence: ICAART 2023},
  city = {Lisbon},
  country = {Portugal},
  pages = {1--8},
  year = {2023},
  publisher={SciTePress},
  organization = {INSTICC}
}

Acknowledgments

This work was supported by the ANR DELEGATION project (grant ANR-21-CE23-0014) of the French Agence Nationale de la Recherche. The authors would like to acknowledge the High Performance Computing Center of the University of Strasbourg for supporting this work by providing scientific support and access to computing resources. Part of the computing resources were funded by the Equipex Equip@Meso project (Programme Investissements d’Avenir) and the CPER Alsacalcul/Big Data. The authors would also like to thank the creators and providers of the UCR Archive.