On the Reproducibility of: "Learning Perturbations to Explain Time Series Predictions"

Published: 24 Jun 2024, Last Modified: 24 Jun 2024Accepted by TMLREveryoneRevisionsBibTeX
Abstract: Deep Learning models have taken the front stage in the AI community, yet explainability challenges hinder their widespread adoption. Time series models, in particular, lack attention in this regard. This study tries to reproduce and extend the work of Enguehard (2023b), focusing on time series explainability by incorporating learnable masks and perturbations. Enguehard (2023b) employed two methods to learn these masks and perturbations, the preservation game (yielding SOTA results) and the deletion game (with poor performance). We extend the work by revising the deletion game’s loss function, testing the robustness of the proposed method on a novel weather dataset, and visualizing the learned masks and perturbations. Despite notable discrepancies in results across many experiments, our findings demonstrate that the proposed method consistently outperforms all baselines and exhibits robust performance across datasets. However, visualizations for the preservation game reveal that the learned perturbations primarily resemble a constant zero signal, questioning the importance of learning perturbations. Nevertheless, our revised deletion game shows promise, recovering meaningful perturbations and, in certain instances, surpassing the performance of the preservation game.
Certifications: Reproducibility Certification
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: Camera ready version: show authors, add link to the repository, update the date and add the OpenReview link.
Code: https://github.com/adamdivak/time_interpret/
Assigned Action Editor: ~Jeremias_Sulam1
Submission Number: 2222