{"ID":2854220,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.15821","arxiv_id":"2510.15821","title":"Chronos-2: From Univariate to Universal Forecasting","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.15821","url_pdf":"https://arxiv.org/pdf/2510.15821v1","authors":"[\"Abdul Fatir Ansari\",\"Oleksandr Shchur\",\"Jaris Küken\",\"Andreas Auer\",\"Boran Han\",\"Pedro Mercado\",\"Syama Sundar Rangapuram\",\"Huibin Shen\",\"Lorenzo Stella\",\"Xiyuan Zhang\",\"Mononito Goswami\",\"Shubham Kapoor\",\"Danielle C. Maddix\",\"Pablo Guerron\",\"Tony Hu\",\"Junming Yin\",\"Nick Erickson\",\"Prateek Mutalik Desai\",\"Hao Wang\",\"Huzefa Rangwala\",\"George Karypis\",\"Yuyang Wang\",\"Michael Bohlke-Schneider\"]","published":"2025-10-17T17:00:53Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"stat.ML\"]","methods":"[]","has_code":false}