Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding
Chronicle is a new multimodal foundation model designed for joint understanding of language and time series data. It is the first model to be pretrained from scratch on both modalities within a unified architecture. Chronicle demonstrates strong performance on various natural language understanding tasks and time series classification benchmarks.
- ▪Chronicle is a 324M-parameter decoder-only transformer model.
- ▪It shares transformer blocks and attention mechanisms between text and time series data.
- ▪The model outperforms existing multimodal baselines in forecasting and classification tasks.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20268 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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Computer Science > Machine Learning arXiv:2605.20268 (cs) [Submitted on 18 May 2026] Title:Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding Authors:Paul Quinlan, Jeremy Levasseur, Qingguo Li, Xiaodan Zhu View a PDF of the paper titled Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding, by Paul Quinlan and 3 other authors View PDF HTML (experimental) Abstract:Real-world time series come with text: metadata, descriptions, news, reports. Yet time series foundation models process numerical sequences in isolation, and the multimodal text-and-time-series models that attempt to bridge the two all adapt a pretrained language model post hoc, inheriting representations shaped without ever seeing temporal data.
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