Shapelet-based Model-agnostic Counterfactual Local Explanations for Time
  Series Classification
Qi Huang Wei Chen Thomas Bäck Niki van Stein
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.