What-If Explanations Over Time: Counterfactuals for Time Series Classification
Counterfactual explanations emerge as a powerful approach in explainable AI, providing what-if scenarios that reveal how minimal changes to an input time series can alter the model's prediction. This work presents a survey of recent algorithms for counterfactual explanations for time series classification. We review state-of-the-art methods, spanning instance-based nearest-neighbor techniques, pattern-driven algorithms, gradient-based optimization, and generative models. For each, we discuss the underlying methodology, the models and classifiers they target, and the datasets on which they are evaluated. We highlight unique challenges in generating counterfactuals for temporal data, such a...
| Authors | Udo Schlegel, Thomas Seidl |
|---|---|
| Primary category | cs.LG |
| Published | 29 March 2026 |
| Last updated at source | 29 March 2026 |
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