Revise, Don't Predict: Environment Response for Time Series Forecasting with Future Exogenous Variables

In time series forecasting, future exogenous variables describe the environment over the forecasting horizon, offering information that historical observations alone cannot provide. Existing forecasters exploit such variables through feature fusion, cross-variable interaction, or conditional generation, focusing mainly on *how* future exogenous information enters a forecaster. We study a complementary question: *what should a dedicated future-environment pathway predict—the full target, or the change to a forecast already supported by history?* We formalize the latter as ***Environment Response***: history first establishes a reference forecast, and the future environment guides how this reference should change. Under squared loss, this role corresponds to the gap between the history-only and environment-aware Bayes predictors, isolating what the future environment adds beyond history. Building on this formulation, we propose **Environment-Guided Flow (EGF)**, which constructs the reference from complementary historical views, encodes the future environment relative to a reference environment, and progressively refines the reference forecast through conditional flow. Beyond EGF, Environment Response can be integrated into existing TSF-X forecasters as a lightweight adapter: on three representative backbones, assigning a matched environment pathway the Response role lowers MSE relative to using it for full-target prediction on 9 of 12 datasets for each backbone, and target-aligned diagnostics show that it moves environment-induced forecast changes closer to the target-required correction. On 12 real-world benchmarks, EGF achieves the best result on 23 of 24 dataset–metric averages.