• agnostic
• time series
• blackbox
• autoencoder
• explainability
• neural network
• interpretability
• black box
• surrogate
• shap

This study presents an agnostic approach to explain the predictions of time series black-box classifiers. Through an autoencoder, this method generates a local neighborhood of the instance to be explained, and then learns a local decision tree classifier to extract rules and counterfactuals. The final explanation is composed by exemplar and counterexemplar time series, and by a shapelet based verbose and graphical decision rule clarifying the black-box decision.