Sub-SpaCE: Subsequence-based Sparse Counterfactual Explanations for Time Series Classification Problems
Mario Refoyo, David Luengo · Research Square · 2023
Abstract The interpretation of existing machine learning models has become a critical task to facilitate the widespread adoption of AI across different domains, leading to the apparition of eXplainable AI (XAI). However, there exists an imbalance between methods tailored for specific data domains, with dedicated approaches for time series data having received limited attention until now. Moreover, current approaches often overlook the unique challenges present in time series data. In this paper, we introduce Subsequence-based Sparse Counterfactual Explanations (Sub-SpaCE), a novel method tailored for time series classification problems. Sub-SpaCE employs genetic algorithms with customized mutation and initialization processes, promoting changes in a small number of subsequences to generate highly sparse and plausible counterfactual explanations. Our empirical evaluations on various datasets demonstrate Sub-SpaCE's superior performance, achieving an optimal balance between sparsity and plausibility in counterfactual explanations for time series data.