Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction
Alessandro Pagani, Marco Cominelli, Liying Han, Gaofeng Dong, Sergio Benini, Francesco Gringoli, Mattia Savardi, Mani B. Srivastava, Trevor Bihl, Erik P. Blasch, Daniel O. Brigham, Kara Combs, Lance M. Kaplan, Federico Cerutti

TL;DR
This paper investigates how the Chronos foundation model processes and represents frequency information in time-series data, providing insights into its internal encoding of signals.
Contribution
It offers the first controlled analysis of Chronos' internal frequency representations using minimal description length probes.
Findings
Chronos captures frequency content across the spectrum.
Representation quality varies across different frequency regimes.
Insights guide practical use and interpretability of Chronos in signal processing.
Abstract
This paper presents a preliminary analysis of the ability of Chronos foundation model to process and internally represent frequency domain information. Foundation models that process time-series data offer practitioners a unified architecture capable of learning generic temporal representations across diverse tasks and domains, reducing the need for task-specific feature engineering and enabling transfer across signal modalities. Despite their growing adoption, the extent to which such models encode fundamental signal properties remains insufficiently characterised. We address this gap by analysing Chronos under controlled conditions, starting from the simplest class of signals: discrete sinusoids generated at fixed frequencies. Using lightweight online minimum description length probes applied to the decoder architecture, we test for the presence and separability of frequency…
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