🤖 AI Summary
This work addresses the lack of temporal structural plausibility in counterfactual explanations for time series classification by proposing a novel input-space gradient-based optimization method. It introduces soft dynamic time warping (soft-DTW) as a constraint to enforce temporal coherence—a first in this context—and incorporates alignment with k-nearest neighbors from the target class. A multi-objective loss function jointly optimizes the counterfactual examples for validity, sparsity, proximity, and temporal structural fidelity. Experimental results demonstrate that the generated counterfactuals not only achieve high classification validity but also significantly outperform existing methods in aligning with the target class distribution and preserving realistic temporal structures.
📝 Abstract
We present a new method for generating plausible counterfactual explanations for time series classification problems. The approach performs gradient-based optimization directly in the input space. To enforce plausibility, we integrate soft-DTW (dynamic time warping) alignment with $k$-nearest neighbors from the target class, which effectively encourages the generated counterfactuals to adopt a realistic temporal structure. The overall optimization objective is a multi-faceted loss function that balances key counterfactual properties. It incorporates losses for validity, sparsity, and proximity, alongside the novel soft-DTW-based plausibility component. We conduct an evaluation of our method against several strong reference approaches, measuring the key properties of the generated counterfactuals across multiple dimensions. The results demonstrate that our method achieves competitive performance in validity while significantly outperforming existing approaches in distributional alignment with the target class, indicating superior temporal realism. Furthermore, a qualitative analysis highlights the critical limitations of existing methods in preserving realistic temporal structure. This work shows that the proposed method consistently generates counterfactual explanations for time series classifiers that are not only valid but also highly plausible and consistent with temporal patterns.