Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification

• URL: http://arxiv.org/abs/2402.01343v1
• Date: Fri, 2 Feb 2024 11:57:53 GMT
• Title: Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification
• Authors: Qi Huang, Wei Chen, Thomas B\"ack, Niki van Stein
• Abstract summary: 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.
• Score: 5.866975269666861
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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