Motif-guided Time Series Counterfactual Explanations
Peiyu Li, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi

TL;DR
This paper introduces MG-CF, a novel method that uses motifs to generate interpretable counterfactual explanations for time series models, enhancing trust and transparency in AI decisions.
Contribution
It is the first to leverage motifs for guiding counterfactual explanation generation in time series analysis.
Findings
MG-CF outperforms state-of-the-art baselines in explanation quality.
The method effectively balances interpretability and fidelity.
Validated on five real-world datasets from UCR.
Abstract
With the rising need of interpretable machine learning methods, there is a necessity for a rise in human effort to provide diverse explanations of the influencing factors of the model decisions. To improve the trust and transparency of AI-based systems, the EXplainable Artificial Intelligence (XAI) field has emerged. The XAI paradigm is bifurcated into two main categories: feature attribution and counterfactual explanation methods. While feature attribution methods are based on explaining the reason behind a model decision, counterfactual explanation methods discover the smallest input changes that will result in a different decision. In this paper, we aim at building trust and transparency in time series models by using motifs to generate counterfactual explanations. We propose Motif-Guided Counterfactual Explanation (MG-CF), a novel model that generates intuitive post-hoc…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Scientific Computing and Data Management · Stock Market Forecasting Methods
