Chronos-2：從單變量預測邁向通用預測

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

預訓練時間序列模型已實現了僅需推理的預測系統，這些系統無需針對特定任務進行訓練即可生成精確預測。然而，現有方法主要集中於單變量預測，限制了其在現實世界場景中的適用性，這些場景中多變量數據和協變量起著至關重要的作用。我們提出了Chronos-2，這是一種能夠以零樣本方式處理單變量、多變量及協變量驅動預測任務的預訓練模型。Chronos-2採用了一種群組注意力機制，通過在群組內多個時間序列之間高效共享信息來促進上下文學習（ICL），這些群組可能代表相關序列集、多變量序列的變量或預測任務中的目標與協變量。這些通用能力是通過在對單變量序列施加多樣多變量結構的合成數據集上訓練而實現的。Chronos-2在三個綜合基準測試中展現了頂尖性能：fev-bench、GIFT-Eval和Chronos Benchmark II。在強調多變量和協變量驅動預測的fev-bench上，Chronos-2的通用ICL能力相較於現有模型帶來了顯著提升。在涉及協變量的任務中，它始終以較大優勢超越基線模型。能源和零售領域的案例研究進一步凸顯了其實際優勢。Chronos-2的上下文學習能力使其成為一種通用預測模型，可在現實世界的預測流程中“即插即用”。

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.