Chronos-2：从单变量预测到通用预测

Chronos-2: From Univariate to Universal Forecasting

October 17, 2025

作者: 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

cs.AI

摘要

预训练时间序列模型已实现了仅需推理的预测系统，无需针对特定任务进行训练即可生成准确预测。然而，现有方法主要集中于单变量预测，限制了其在现实世界场景中的适用性，这些场景中多变量数据和协变量起着至关重要的作用。我们提出了Chronos-2，一种能够以零样本方式处理单变量、多变量及协变量信息预测任务的预训练模型。Chronos-2采用了一种群体注意力机制，通过高效地在组内多个时间序列间共享信息，促进了上下文学习（ICL），这些组可能代表相关序列集、多变量序列的不同变量，或预测任务中的目标与协变量。这些通用能力是通过在合成数据集上训练实现的，这些数据集在单变量序列上施加了多样化的多变量结构。Chronos-2在三个综合基准测试中展现了最先进的性能：fev-bench、GIFT-Eval和Chronos Benchmark II。在强调多变量和协变量信息预测的fev-bench上，Chronos-2的通用ICL能力带来了相较于现有模型的显著提升。在涉及协变量的任务中，它始终以较大优势超越基线模型。能源和零售领域的案例研究进一步凸显了其实用优势。Chronos-2的上下文学习能力确立了其作为通用预测模型的地位，可直接应用于现实世界的预测流程中。

English

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.