Foundation models mark a profound paradigm shift in time series modeling, with task-specific models being superseded by general-purpose zero-shot models. Yet, current approaches primarily focus on forecasting, while real-world time series are often irregularly and partially observed, requiring models that can jointly forecast, impute missing values, and handle degraded sampling conditions. To address these challenges, we introduce TS-ICL, a novel probabilistic In-Context Learning encoder--regressor Transformer that unifies forecasting and imputation. TS-ICL formulates time series tasks as timestamp-aligned regression and naturally incorporates covariates by training on synthetic dependency structures generated from a novel causal data prior. Empirically, TS-ICL achieves a new state-of-the-art in imputation, while remaining competitive with leading forecasting foundation models across both univariate and covariate-aware benchmarks. It shows particularly strong performance in forecasting with partially observed look-back windows.

翻译：基础模型标志着时间序列建模领域的深刻范式转变，任务特定模型正逐渐被通用零样本模型所取代。然而，当前方法主要聚焦于预测任务，而现实世界中的时间序列通常是非规则且部分可观测的，因此需要能够同时进行预测、缺失值插补以及处理退化采样条件的模型。为应对这些挑战，我们提出了TS-ICL，一种新颖的概率性上下文学习编码器-回归器Transformer，它统一了预测与插补任务。TS-ICL将时间序列任务表述为时间戳对齐的回归问题，并通过在由新颖因果数据先验生成的合成依赖结构上进行训练，自然地融合协变量。实验表明，TS-ICL在插补任务上达到了新的最优水平，同时在单变量和协变量感知基准测试中，与领先的预测基础模型保持竞争力。它在部分观测回看窗口的预测任务中表现出尤为强劲的性能。