Chronos-2: From Univariate to Universal Forecasting
Pretrained time series models have enabled inference-only forecasting systems
that produce accurate predictions without task-specific training. However,
existing approaches largely focus on univariate forecasting, limiting their
applicability in real-world scenarios where multivariate data and covariates
play a crucial role. We present Chronos-2, a pretrained model capable of
handling univariate, multivariate, and covariate-informed forecasting tasks in
a zero-shot manner. Chronos-2 employs a group attention mechanism that
facilitates in-context learning (ICL) through efficient information sharing
across multiple time series within a group, which may represent sets of related
series, variates of a multivariate series, or targets and covariates in a
forecasting task. These general capabilities are achieved through training on
synthetic datasets that impose diverse multivariate structures on univariate
series. Chronos-2 delivers state-of-the-art performance across three
comprehensive benchmarks: fev-bench, GIFT-Eval, and Chronos Benchmark II. On
fev-bench, which emphasizes multivariate and covariate-informed forecasting,
Chronos-2's universal ICL capabilities lead to substantial improvements over
existing models. On tasks involving covariates, it consistently outperforms
baselines by a wide margin. Case studies in the energy and retail domains
further highlight its practical advantages. The in-context learning
capabilities of Chronos-2 establish it as a general-purpose forecasting model
that can be used "as is" in real-world forecasting pipelines.
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Abdul Fatir Ansari
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Oleksandr Shchur
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2025-10-21T14:04:42.117Z
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Oleksandr Shchur
user
shchuro
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Jaris Küken
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Andreas Auer
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Boran Han
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Pedro Mercado
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Syama Sundar Rangapuram
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Huibin Shen
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Lorenzo Stella
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Xiyuan Zhang
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Mononito Goswami
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Shubham Kapoor
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Danielle C. Maddix
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Pablo Guerron
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Tony Hu
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Junming Yin
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Nick Erickson
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Prateek Mutalik Desai
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Hao Wang
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Huzefa Rangwala
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George Karypis
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Yuyang Wang
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Michael Bohlke-Schneider
Chronos-2: From Univariate to Universal Forecasting
Pretrained time series models have enabled inference-only forecasting systems
that produce accurate predictions without task-specific training. However,
existing approaches largely focus on univariate forecasting, limiting their
applicability in real-world scenarios where multivariate data and covariates
play a crucial role. We present Chronos-2, a pretrained model capable of
handling univariate, multivariate, and covariate-informed forecasting tasks in
a zero-shot manner. Chronos-2 employs a group attention mechanism that
facilitates in-context learning (ICL) through efficient information sharing
across multiple time series within a group, which may represent sets of related
series, variates of a multivariate series, or targets and covariates in a
forecasting task. These general capabilities are achieved through training on
synthetic datasets that impose diverse multivariate structures on univariate
series. Chronos-2 delivers state-of-the-art performance across three
comprehensive benchmarks: fev-bench, GIFT-Eval, and Chronos Benchmark II. On
fev-bench, which emphasizes multivariate and covariate-informed forecasting,
Chronos-2's universal ICL capabilities lead to substantial improvements over
existing models. On tasks involving covariates, it consistently outperforms
baselines by a wide margin. Case studies in the energy and retail domains
further highlight its practical advantages. The in-context learning
capabilities of Chronos-2 establish it as a general-purpose forecasting model
that can be used "as is" in real-world forecasting pipelines.
Chronos-2, a pretrained model with a group attention mechanism, achieves state-of-the-art performance in zero-shot univariate, multivariate, and covariate-informed forecasting tasks.
pretrained model group attention mechanism in-context learning ICL synthetic datasets multivariate structures fev-bench GIFT-Eval Chronos Benchmark II energy domain retail domain