TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning
Provides a flexible foundation model for time series that handles missing data and covariates, benefiting practitioners dealing with real-world irregular sampling.
TS-ICL introduces a probabilistic in-context learning transformer that unifies forecasting and imputation for irregularly observed time series, achieving state-of-the-art imputation and competitive forecasting performance.
Foundation models mark a profound paradigm shift in time series modeling, with task-specific models being superseded by general-purpose zero-shot models.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.