Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915989547909120 |
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| author | Pagani, Alessandro Cominelli, Marco Han, Liying Dong, Gaofeng Benini, Sergio Gringoli, Francesco Savardi, Mattia Srivastava, Mani B. Bihl, Trevor Blasch, Erik P. Brigham, Daniel O. Combs, Kara Kaplan, Lance M. Cerutti, Federico |
| author_facet | Pagani, Alessandro Cominelli, Marco Han, Liying Dong, Gaofeng Benini, Sergio Gringoli, Francesco Savardi, Mattia Srivastava, Mani B. Bihl, Trevor Blasch, Erik P. Brigham, Daniel O. Combs, Kara Kaplan, Lance M. Cerutti, Federico |
| contents | 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 information in the model's internal representations. The results provide insight into how frequential content is captured across the frequency spectrum and highlight regimes in which representation quality may degrade or require particular care. These findings offer practical guidance for users of Chronos in signal processing and information fusion contexts, and contribute to ongoing efforts to improve the interpretability and evaluation of foundation models for temporal data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06361 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction Pagani, Alessandro Cominelli, Marco Han, Liying Dong, Gaofeng Benini, Sergio Gringoli, Francesco Savardi, Mattia Srivastava, Mani B. Bihl, Trevor Blasch, Erik P. Brigham, Daniel O. Combs, Kara Kaplan, Lance M. Cerutti, Federico Machine Learning 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 information in the model's internal representations. The results provide insight into how frequential content is captured across the frequency spectrum and highlight regimes in which representation quality may degrade or require particular care. These findings offer practical guidance for users of Chronos in signal processing and information fusion contexts, and contribute to ongoing efforts to improve the interpretability and evaluation of foundation models for temporal data. |
| title | Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.06361 |