AnchorScope: Learning Textual Forecast Revisions at the Right Temporal Scale

Forecasting systems often begin with a prediction from numerical history and then revise it when contemporaneous reports reveal conditions that the observed trajectory has not yet fully expressed. Text is useful in this role when it is correctly paired in time, relevant to the current numerical dynamics, and prevented from overwhelming the numerical evidence. We formulate text-aware forecasting as forecast revision: numerical history establishes a reference from which the horizon is predicted, and paired text proposes a limited update to that reference. AnchorScope distils document histories into semantic candidates, routes them to ordered adaptive frequency bands, and calibrates each revision with bounded amplitude, phase, and residual components. Under a row-level causal availability rule for Climate, aligned text outperforms no text, shuffled text, and random text. Controlled retraining holds encoders, text modulators, budgets, and seeds fixed: anchored adaptive bands outperform fixed, anchor-free, and global allocation rules on four datasets, and configured bounds outperform a threefold relaxation. Broad comparisons on nine Time-MMD datasets show that these revisions retain competitive forecasting performance. AnchorScope learns where a textual revision belongs in the numerical spectrum and how large that revision should be.