Instance-based Counterfactual Explanations for Time Series Classification

TL;DR

This paper introduces Native Guide, a model-agnostic, case-based method for generating counterfactual explanations for time series classifiers, improving interpretability of black-box models in this domain.

Contribution

It presents a novel instance-based approach tailored for time series data, addressing the gap in explainability methods for such models.

Findings

Native Guide produces plausible, proximal, sparse, and diverse explanations.

It outperforms key benchmark counterfactual methods in experiments.

The approach is model-agnostic and adaptable to different classifiers.

Abstract

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.