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
Organization: Amazon

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.