AB-CF: Attention-based Counterfactual Explanation for Multivariate Time Series

This is the repository for our paper titled "Attention-Based Counterfactual Explanation for Multivariate Time Series". This paper has been accepted at the Big Data Analytics and Knowledge Discovery 25th International Conference, DaWaK 2023

Abstract

In this paper, we propose Attention-based Counterfactual Explanation (AB-CF), a novel model that generates post-hoc counterfactual explanations for multivariate time series classification that narrows the attention to a few important segments. We validated our model using seven real-world time-series datasets from the UEA repository. Our experimental results show the superiority of AB-CF in terms of validity, proximity, sparsity, contiguity, and efficiency compared with other competing state-of-the-art baselines.

Approach

Dataset

The data used in this project comes from the UEA archive.

Prerequisites

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

Reference

If you re-use this work, please cite:

@inproceedings{li2023attention, title={Attention-Based Counterfactual Explanation for Multivariate Time Series}, author={Li, Peiyu and Bahri, Omar and Boubrahimi, Souka{"\i}na Filali and Hamdi, Shah Muhammad}, booktitle={International Conference on Big Data Analytics and Knowledge Discovery}, pages={287--293}, year={2023}, organization={Springer} }