XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification

This repository contains the Python implementation of XEM as described in the paper XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification.

Requirements

XEM has been implemented in Python 3.8 with the following packages:

• lcensemble
• pyarrow
• pyyaml

Usage

Run main.py with the following argument:

• configuration: name of the configuration file (string)

python main.py --config configuration/config.yml

The current configuration file provides an example of classification with XEM on the Basic Motions UEA dataset with the configuration presented in the paper, and an example of identification of the time window used to classify the first MTS of the test set.

Citation

@article{Fauvel22XEM,
  author = {Fauvel, K. and E. Fromont and V. Masson and P. Faverdin and A. Termier},
  title = {XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification},
  journal = {Data Mining and Knowledge Discovery},
  year = {2022},
  volume = {36},
  number = {3},
  pages = {917-957}
}