Behind Zero-Shot Forecasting: How Do Time-Series Foundation Models Use Their Context?

Time-series foundation models (TSFMs) leverage large-scale pretraining to achieve cross-domain generalization in forecasting. Among them, the zero-shot paradigm generates predictions using only the observed context, without any task-specific training or fine-tuning. This enables immediate deployment, but leaves a fundamental question open: how do these models use their context to produce forecasts? We investigate this in-context forecasting mechanism directly. Based on an architecture-agnostic framework that characterizes context use through controlled input interventions and their effects on the forecast, we establish three findings. First, TSFM forecasts rule out context parroting, indicating that the models perform inference rather than merely interpolate among values already observed in the context. Second, context influence is governed primarily by temporal position instead of phase-space distance, while similarity-based sample selection can admit a local-approximation error bound no larger than recency-based sample selection. Third, despite its influence decay with temporal distance in the majority of models, distant context can remain functionally important by providing temporal structure beyond distributional statistics. Collectively, these results reveal both the functional strengths and intrinsic limitations of current TSFMs in context utilization, providing a mechanistic basis for diagnosing zero-shot forecasting behavior and informing the design of future models and training strategies.