Chronos-2: From Univariate to Universal Forecasting
Authors: name: Abdul Fatir Ansari; name: Oleksandr Shchur; name: Jaris Küken; name: Andreas Auer; name: Boran Han; name: Pedro Mercado; name: Syama Sundar Rangapuram; name: Huibin Shen; name: Lorenzo Stella; name: Xiyuan Zhang; name: Mononito Goswami; name: Shubham Kapoor; name: Danielle C. Maddix; name: Pablo Guerron; name: Tony Hu; name: Junming Yin; name: Nick Erickson; name: Prateek Mutalik Desai; name: Hao Wang; name: Huzefa Rangwala; name: George Karypis; name: Yuyang Wang; name: Michael Bohlke-Schneider
Published: 2025-10-17 17:00:53

Abstract: 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.