Chronos-2: A foundation model for multivariate time series forecasting
Abstract
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
Chronos-2 introduces a multivariate time series foundation model leveraging group attention and synthetic pretraining to achieve state-of-the-art zero-shot forecasting across diverse scientific domains. The paper demonstrates that a single pretrained model can effectively capture complex multivariate interactions and external drivers without task-specific training, outperforming specialized baselines in epidemiology, macroeconomics, earth science, and hydrology, while maintaining high computational efficiency.
Methodology
The paper introduces Chronos-2, a 120M parameter encoder-only Transformer foundation model for time series forecasting. The core methodological contribution is the "group attention" mechanism, which allows the model to infer dependencies among multivariate time series and heterogeneous covariates directly from historical context (in-context learning) without task-specific training. The architecture alternates between temporal attention (within a series) and group attention (across related series). A significant portion of the methodology focuses on the synthetic data generation pipeline, which creates diverse multivariate dynamics by coupling univariate generators (Gaussian Processes, AR, ETS) through linear (VAR, mixing matrices) and non-linear (MLP, RNN) transformations. This approach addresses the scarcity of large-scale multivariate time series data for pretraining. The model uses a quantile regression head to provide probabilistic forecasts, supporting uncertainty quantification.
Results & evaluation
The evaluation is extensive and rigorous, covering both standard benchmarks and domain-specific case studies. On the FEV benchmark (100 tasks), Chronos-2 achieves state-of-the-art results, outperforming strong baselines like TiRex, TimesFM, and Moirai. It demonstrates significant gains in win rate and skill score over univariate inference, validating the group attention mechanism. The paper includes four detailed case studies: epidemiology (ECDC RespiCast), macroeconomics (German CPI), earth science (FLUXNET carbon flux), and hydrology (CAMELS-DE rainfall-runoff). In each domain, the model matches or surpasses specialized, task-specific models (e.g., HBV, LSTM ensembles, XGBoost) in zero-shot settings. The inclusion of hierarchical forecasting experiments further demonstrates the model's ability to capture cross-level dependencies without explicit hierarchy information. The computational efficiency is also highlighted, with inference times under 1 second for 100 series on a mid-range GPU.
Reproducibility
The paper provides detailed architectural specifications, training hyperparameters, and data generation procedures. The model size (120M parameters) and training setup (4x A100 GPUs) are clearly stated. The synthetic data generation process is described in detail, allowing for potential replication of the pretraining corpus. However, the specific code repository or model weights are not explicitly linked in the provided text (though likely available given the Amazon affiliation and prior Chronos releases). The evaluation protocols for the case studies are well-defined, referencing standard domain metrics (WIS, NSE, etc.).
Limitations
The model is limited to 120M parameters, which is smaller than some competing foundation models (e.g., Moirai, TimesFM), potentially limiting its capacity for extremely complex dynamics. The reliance on synthetic data for multivariate pretraining may introduce a domain gap if real-world interactions are more complex than the synthesized ones. The paper does not extensively discuss the interpretability of the inferred relationships, which is a known challenge in foundation models. Additionally, the model does not natively support static metadata (e.g., soil type, demographics), which is a limitation for certain scientific applications like ungauged basin prediction, though the authors acknowledge this as future work.
Broader impact
This paper has high potential impact by democratizing advanced forecasting capabilities. By providing a single, efficient, zero-shot model that handles multivariate interactions, it reduces the barrier to entry for applying state-of-the-art forecasting in scientific and industrial domains. The demonstrated success in diverse fields (epidemiology, economics, earth sciences) suggests broad applicability. The efficiency of the model (running on consumer GPUs/CPU) makes it accessible for edge deployment or resource-constrained environments. It shifts the paradigm from task-specific model fitting to contextual inference, which could streamline workflows in data science teams. Chronos-2 introduces a multivariate time series foundation model leveraging group attention and synthetic pretraining to achieve state-of-the-art zero-shot forecasting across diverse scientific domains. The paper demonstrates that a single pretrained model can effectively capture complex multivariate interactions and external drivers without task-specific training, outperforming specialized baselines in epidemiology, macroeconomics, earth science, and hydrology, while maintaining high computational efficiency.
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