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

arXiv (Cornell University)

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.

Authors 4

1. Qi Huang Aachen

   Leiden University · University of Applied Sciences Leiden

   Affiliation as printed

   LIACS , Leiden University Niels Bohrweg 1 , 2333 CA , Leiden The Netherlands

2. Wei Chen Aachen

   Leiden University · University of Applied Sciences Leiden

   Affiliation as printed

   LIACS , Leiden University Niels Bohrweg 1 , 2333 CA , Leiden The Netherlands

3. Thomas Bäck
4. Bas van Stein Aachen

   Leiden University · University of Applied Sciences Leiden

   Affiliation as printed

   LIACS , Leiden University Niels Bohrweg 1 , 2333 CA , Leiden The Netherlands