GALACTIC: Global and Local Agnostic Counterfactuals for Time-series Clustering
#counterfactuals #time-series #clustering #interpretability #model-agnostic #AI #machine learning #GALACTIC
📌 Key Takeaways
- GALACTIC introduces a method for generating counterfactual explanations in time-series clustering.
- It provides both global and local explanations to enhance interpretability of clustering results.
- The approach is model-agnostic, applicable to various clustering algorithms without retraining.
- It aims to improve trust and understanding in AI-driven time-series analysis applications.
📖 Full Retelling
arXiv:2603.05318v1 Announce Type: cross
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 GALA
🏷️ Themes
AI Explainability, Time-series Analysis
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Original Source
--> Computer Science > Machine Learning arXiv:2603.05318 [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 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 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 , GALACTIC generates perturbations via a cluster-aware optimization objective that respects the target and underlying cluster assignments. At cluster level , to mitigate cognitive load and enhance interpretability, we formulate a representative CE selection problem. We propose a Minimum Description Length objective to extract a non-redundant summary of global explanations that characterize the transitions between clusters. We prove that our MDL objective is supermodular, which allows the corresponding MDL reduction to be framed as a monotone submodular set function. This enables an efficient greedy selection algorithm with provable $(1-1/e)$ approximation guarantees. Extensive experimental evaluation on the UCR Archive demonstrates that GALACTIC produces significantly sparser local CEs and more concise global summaries than state-of-the-art baselines adapted for our problem, offering the first unified approach for interpreting clustered time-series throu...
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