TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning

• URL: http://arxiv.org/abs/2606.05878v1
• Date: Thu, 04 Jun 2026 08:52:21 GMT
• Title: TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning
• Abstract summary: Current approaches primarily focus on forecasting, while real-world time series are often irregularly and partially observed.<n>We introduce TS-ICL, a novel probabilistic In-Context Learning encoder--regressor Transformer that unifies forecasting and imputation.
• Score: 0.764671395172401
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation models mark a profound paradigm shift in time series modeling, with task-specific models being superseded by general-purpose zero-shot models.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.

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