Beyond One-Shot Forecasting: Predict-then-Reveal Time Series Prediction

Long-term time-series forecasting typically predicts the entire future horizon in one shot from a fixed historical context. However, as the forecast unfolds, earlier observations within the prediction horizon become available and can provide valuable evidence for refining the still-unseen future. We introduce Predict-then-Reveal Forecasting, a forecasting paradigm in which predictions are progressively committed, revealed, and subsequently used to improve later forecasts. Under this paradigm, we propose PRISM, a progressive forecasting framework that maintains multiple candidate future trajectories and continuously updates them using newly revealed observations. At each stage, PRISM jointly adjusts the relative beliefs over candidate futures according to their consistency with observed feedback and revises their unrevealed trajectories based on the resulting residual patterns. To learn effective intermediate revisions, PRISM further exploits complete future outcomes as privileged supervision during training, while inference relies exclusively on observations that have already been revealed. This design enables forecasts to evolve without test-time parameter optimization or access to future targets. Extensive experiments across diverse real-world time-series benchmarks and prediction horizons demonstrate that PRISM consistently outperforms strong standard forecasting and online adaptation methods.