Chronos-2：面向多变量时间序列预测的基础模型
Chronos-2: A foundation model for multivariate time series forecasting
Chronos-2 是一个面向零样本预测的时间序列基础模型，可直接从历史上下文推断多条时间序列之间以及异构协变量之间的关系，并生成概率预测。论文称其在多项基准上平均技能分数比最强基线高 5.2 个百分点、胜率提升 10 个百分点，在单块中端 GPU 上不到一秒即可为 100 条时间序列生成预测。该模型还在传染病预测、宏观经济建模、碳通量预测和径流模拟等场景中与各领域专用方法持平或更优。
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, Florian Saupe, Markus Reichstein, Tony Hu, Junming Yin, Nick Erickson, Prateek Mutalik Desai, Hao Wang, Huzefa Rangwala, George Karypis, Yuyang Wang, Michael Bohlke-Schneider
Abstract:Accurate forecasting with uncertainty quantification is critical in a variety of scientific applications ranging from epidemiology and economics to energy systems and earth sciences. Yet most forecasting models still need to be developed and trained separately for each domain-specific dataset. Time series foundation models (TSFMs) aim to remove this limitation and enable the application of the same model across domains through large-scale pretraining. However, most existing TSFMs treat each time series independently and therefore cannot model interactions among time series and external drivers. Here, we introduce Chronos-2, a foundation model for zero-shot forecasting that models the interactions between multivariate time series and heterogeneous covariates, which commonly arise in scientific applications. The model infers relationships among time series directly from historical context and generates probabilistic forecasts without task-specific treatment. Across various benchmarks, Chronos-2 achieves state-of-the-art accuracy on average by improving skill scores by 5.2 percentage points relative to the strongest baselines and increasing win rates by 10 percentage points. Chronos-2 is also computationally efficient, generating forecasts for 100 time series in under one second on a single mid-range GPU, 50x and 1000x faster end-to-end than the best task-specific point and probabilistic forecasters, respectively. In addition, we demonstrate its effectiveness in diverse scientific settings -- including infectious-disease forecasting, macroeconomic modeling, carbon-flux prediction, and runoff simulation -- where the model matches or surpasses specialized methods developed for each domain. These results show that a single pretrained model can capture structure shared across temporal systems.
| Comments: | 41 pages, 9 figures. Extended version of the Chronos-2 technical report (arXiv:2510.15821), adding scientific case studies (epidemiology, macroeconomics, carbon flux, hydrology, hierarchical forecasting), additional baselines, ablation studies, and calibration analysis. Code: this https URL |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML) |
| Cite as: | arXiv:2610.41956 [cs.LG] |
| (or arXiv:2610.41956v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.41956 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Abdul Fatir Ansari [view email]
[v1]
Fri, 30 Oct 2026 16:08:23 UTC (1,919 KB)
来源：arXiv cs.AI · arxiv.org