Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification
<|reference_start|>Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification: In this work, we propose a model-agnostic instance-based post-hoc explainability method for time series classification. The proposed algorithm, namely Time-CF, leverages shapelets and TimeGAN to provide counterfactual explanations for arbitrary time series classifiers. We validate the proposed method on several real-world univariate time series classification tasks from the UCR Time Series Archive. The results indicate that the counterfactual instances generated by Time-CF when compared to state-of-the-art methods, demonstrate better performance in terms of four explainability metrics: closeness, sensibility, plausibility, and sparsity.<|reference_end|>
@article{huang2024shapelet-based,
    title={Shapelet-based Model-agnostic Counterfactual Local Explanations for Time
  Series Classification},
    author={Qi Huang, Wei Chen, Thomas B"ack, Niki van Stein},
    journal={arXiv preprint arXiv:2402.01343},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01343},
    primaryClass={cs.LG}
}