Parallax: Correcting Time Series Forecasts from Cross-Resolution Disagreement

In long-term time series forecasting, a fine-resolution forecast, when aggregated, can disagree with a forecast produced directly at a coarser resolution for the same future interval. This disagreement is available at prediction time and carries information about fine-resolution forecast errors. We present Parallax, a post-hoc, model-agnostic method that uses cross-resolution disagreement to correct the fine-resolution forecasts of frozen backbones, whether pretrained foundation models or task-specific deep forecasters trained on the target dataset. Parallax decomposes the disagreement into the aggregated fine residual and the coarse-forecast error, reducing forecast correction to residual estimation, and fits an affine corrector whose moments combine a resolution-structured covariance, built from a nested aggregation chain, with empirical moments estimated from historical forecast–target pairs. At test time, Parallax uses only the fine- and coarse-resolution forecasts, requiring neither future observations nor any updating to the backbone. Evaluated on 13 benchmark datasets with 7 task-specific models and 5 pretrained foundation models, Parallax reduces MSE by 11.8%-26.4% across task-specific backbones and by 33.2% on average across foundation models.