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Chronos-2: A foundation model for multivariate time series forecasting — arXiv:2610.41956

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41956
License (as archived): CC0-1.0
Archived: 2026-11-03T05:33:38.957Z
SHA-256 of the archived text: e535b257dac93da17d5624f6ab55dec5413fa3bb43878281138108f9801a1950
Stable URL of this page: https://metraipolis.com/documento/dc_fc50e53373bf

How to cite

Chronos-2: A foundation model for multivariate time series forecasting — arXiv:2610.41956 — arXiv (en), https://arxiv.org/abs/2610.41956 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fc50e53373bf (Metraipolis archive dc_fc50e53373bf, sha256 e535b257dac93da17d5624f6ab55dec5413fa3bb43878281138108f9801a1950)

BibTeX

@misc{dcfc50e53373bf,
  title = {Chronos-2: A foundation model for multivariate time series forecasting — arXiv:2610.41956},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41956},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fc50e53373bf; sha256 e535b257dac93da17d5624f6ab55dec5413fa3bb43878281138108f9801a1950},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41956. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Accurate forecasting with uncertainty quantification is critical in a variety of scientific applications ranging from epidemiology and economics to energy systems and earth sciences. Yet most forecasting models still need to be developed and trained separately for each domain-specific dataset. Time series foundation models (TSFMs) aim to remove this limitation and enable the application of the same model across domains through large-scale pretraining. However, most existing TSFMs treat each time series independently and therefore cannot model interactions among time series and external drivers. Here, we introduce Chronos-2, a foundation model for zero-shot forecasting that models the interactions between multivariate time series and heterogeneous covariates, which commonly arise in scientific applications. The model infers relationships among time series directly from historical context and generates probabilistic forecasts without task-specific treatment. Across various benchmarks, Chronos-2 achieves state-of-the-art accuracy on average by improving skill scores by 5.2 percentage points relative to the strongest baselines and increasing win rates by 10 percentage points. Chronos-2 is also computationally efficient, generating forecasts for 100 time series in under one second on a single mid-range GPU, 50x and 1000x faster end-to-end than the best task-specific point and probabilistic forecasters, respectively. In addition, we demonstrate its effectiveness in diverse scientific settings -- including infectious-disease forecasting, macroeconomic modeling, carbon-flux prediction, and runoff simulation -- where the model matches or surpasses specialized methods developed for each domain. These results show that a single pretrained model can capture structure shared across temporal systems.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Machine Learning (stat.ML).
Pagina del lavoro: https://arxiv.org/abs/2610.41956 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T05:18:52.236Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).