Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification
This provides more interpretable explanations for time series classifiers, which is useful for domain experts in fields like healthcare or finance, though it is incremental as it builds on existing shapelet and GAN techniques.
The authors tackled the problem of explaining time series classification models by proposing Time-CF, a model-agnostic method that uses shapelets and TimeGAN to generate counterfactual explanations, resulting in better performance on four explainability metrics compared to state-of-the-art methods.
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.