[2603.05318] GALACTIC: Global and Local Agnostic Counterfactuals for Time-series Clustering

Computer Science > Machine Learning arXiv:2603.05318 (cs) [Submitted on 5 Mar 2026] Title:GALACTIC: Global and Local Agnostic Counterfactuals for Time-series Clustering Authors:Christos Fragkathoulas, Eleni Psaroudaki, Themis Palpanas, Evaggelia Pitoura View a PDF of the paper titled GALACTIC: Global and Local Agnostic Counterfactuals for Time-series Clustering, by Christos Fragkathoulas and 3 other authors View PDF HTML (experimental) Abstract: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 introduces GALACTIC, the first unified framework to bridge local and global counterfactual explainability for unsupervised time-series clustering. At instance level (local), GALACTIC generates perturbations via a cluster-aware optimization objective that respects the target and underlying cluster assignments. At cluster level (global), to mitigate cognitive load and enhance interpretability, we formulate a representative CE selection problem. We propose a Minimum Description Length (MDL) objective to extract a non-redundant summary of global explanations that characterize the transitions between clusters. W...