AI RESEARCH
GALACTIC: Global and Local Agnostic Counterfactuals for Time-series Clustering
arXiv CS.LG
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ArXi:2603.05318v2 Announce Type: replace Time-series clustering is a fundamental tool for pattern discovery, yet existing explainability methods, primarily based on feature attribution or metadata, fail to identify the transitions that move an instance across cluster boundaries. While Counterfactual Explanations (CEs) identify the minimal temporal perturbations required to alter the prediction of a model, they have been mostly confined to supervised settings. This paper