Title: Adapting and Explaining Time Series Foundation Models with Concepts
Primary area: interpretability and explainable AI
Keywords: Time Series, Explainability, Foundation Models

Abstract: Foundation models for time series have achieved impressive performance on high-dimensional temporal data, but their opacity remains a barrier to adoption in domains where transparency and trust are essential. While Concept Activation Vectors (CAVs) have proven effective for aligning model predictions with human-understandable concepts in vision, their application to temporal data is largely unexplored due to the complexity of sequential dynamics and the challenge of defining meaningful temporal concepts.
In this work, we propose COFT, the first framework that adapts CAVs to foundation models for time series. COFT  discovers dataset-specific temporal concepts through shapelet-based transformations and organizes them into a concept bank, enabling alignment between temporal subsequences and class labels. To integrate these concepts efficiently, COFT employs Low-Rank Adaptation (LoRA), allowing foundation models to internalize temporal concepts without sacrificing scalability or efficiency. Our experiments on diverse real-world benchmarks demonstrate that COFT not only improves interpretability quantified through concept alignment and CAV-based metrics, but also enhances predictive performance compared to state-of-the-art foundation models. Beyond accuracy, COFT provides a mechanism for human-centered explanations by linking model decisions to temporally meaningful concepts, offering practitioners new tools for diagnosis, trust, and refinement. Our work establishes a foundation for concept-based interpretability in time series modeling, bridging the gap between predictive power and human understanding.

TL;DR: COFT makes time-series foundation models interpretable by adapting Concept Activation Vectors, boosting both trust and accuracy