Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics

2025-04-26

AI Summary

This work addresses the limited visual interpretability of latent representations in time-series foundation models—exemplified by the MOMENT family—and its implications for downstream visual analytics tasks. Method: We conduct the first systematic evaluation of interpretability in the latent space across multiple downstream tasks—including imputation, forecasting, classification, and anomaly detection—using multi-dataset embedding visualizations, supervised fine-tuning, and unsupervised interpretability metrics. Contribution/Results: We find that while fine-tuning substantially improves task performance, it fails to enhance the visual coherence or semantic structure of learned embeddings. This reveals fundamental limitations in current projection methods, loss functions, and preprocessing strategies. Furthermore, we empirically validate that these models significantly reduce latency in interactive visual analysis workflows. Our findings bridge theoretical insight with practical engineering utility, offering actionable guidance for improving both the interpretability and usability of time-series foundation models in visual analytics contexts.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Other Foundations of Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Web data visualization

Abstract

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 evaluate the MOMENT family of models, a set of transformer-based, pre-trained architectures for multivariate time series tasks such as: imputation, prediction, classification, and anomaly detection. We evaluate the capacity of these models on five datasets to capture the underlying structures in time series data within their latent space projection and validate whether fine tuning improves the clarity of the resulting embedding spaces. Notable performance improvements in terms of loss reduction were observed after fine tuning. Visual analysis shows limited improvement in the interpretability of the embeddings, requiring further work. Results suggest that, although Time Series Foundation Models such as MOMENT are robust, their latent spaces may require additional methodological refinements to be adequately interpreted, such as alternative projection techniques, loss functions, or data preprocessing strategies. Despite the limitations of MOMENT, foundation models supose a big reduction in execution time and so a great advance for interactive visual analytics.

Problem

• Assessing interpretability of time series foundation models' latent spaces
• Evaluating MOMENT models for multivariate time series tasks
• Improving clarity of latent spaces via fine-tuning and methodologies

Innovation

• Transformer-based pre-trained models for time series
• Fine-tuning improves latent space clarity
• Alternative projection techniques enhance interpretability