Dissecting Chronos: Sparse Autoencoders Reveal Causal Feature Hierarchies in Time Series Foundation Models

Researchers applied sparse autoencoders to analyze Chronos-T5-Large, a 710M parameter time series foundation model, revealing how different layers process temporal data. The study found that mid-encoder layers contain the most causally important features for change detection, while early layers handle frequency patterns and final layers compress semantic concepts.