Chronos-2: A foundation model for multivariate time series forecasting
[Uczenie maszynowe] Autorzy: Abdul Fatir Ansari, Oleksandr Shchur, Jaris Küken i 22 więcej
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…
- Kategoria arxiv
- cs.LG
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- Uczenie maszynowe