Instance-based Counterfactual Explanations for Time Series Classification
This addresses the need for interpretability in time series AI systems, which is crucial for domains like healthcare or finance, but it is incremental as it adapts existing counterfactual methods to a specific data type.
The paper tackles the problem of explaining predictions by black-box AI systems for time series classification, which has received less attention compared to image and tabular data, and proposes Native Guide, a model-agnostic, case-based technique that generates counterfactual explanations. The result shows that Native Guide produces plausible, proximal, sparse, and diverse explanations that outperform key benchmark methods in comparative experiments.
In recent years, there has been a rapidly expanding focus on explaining the predictions made by black-box AI systems that handle image and tabular data. However, considerably less attention has been paid to explaining the predictions of opaque AI systems handling time series data. In this paper, we advance a novel model-agnostic, case-based technique -- Native Guide -- that generates counterfactual explanations for time series classifiers. Given a query time series, $T_{q}$, for which a black-box classification system predicts class, $c$, a counterfactual time series explanation shows how $T_{q}$ could change, such that the system predicts an alternative class, $c'$. The proposed instance-based technique adapts existing counterfactual instances in the case-base by highlighting and modifying discriminative areas of the time series that underlie the classification. Quantitative and qualitative results from two comparative experiments indicate that Native Guide generates plausible, proximal, sparse and diverse explanations that are better than those produced by key benchmark counterfactual methods.