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cs.LG2025
Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics
Inmaculada Santamaria-Valenzuela, Victor Rodriguez-Fernandez, Javier Huertas-Tato +2
The present study explores the interpretability of latent spaces produced by time series foundation models, focusing on their potential for visual analysis tasks. Specifically, we…
cs.LG2024
Exploring Scalability in Large-Scale Time Series in DeepVATS framework
Inmaculada Santamaria-Valenzuela, Victor Rodriguez-Fernandez, David Camacho
Visual analytics is essential for studying large time series due to its ability to reveal trends, anomalies, and insights. DeepVATS is a tool that merges Deep Learning (Deep) with…