HIP: Forecast-Calibrated Event Representations for Multi-Horizon Time Series Forecasting

Forecast-relevant events do not by themselves specify how their influence should be represented across a future trajectory. We introduce Hierarchical Impact Pathways (HIP), a structured intermediate representation that maps semantic event hypotheses and temporal attributes into horizon-wise conditioning signals for multi-horizon forecasting. HIP explicitly separates what an event is hypothesized to affect from how its influence is represented across future prediction steps. Because semantically plausible event descriptions are not necessarily predictively useful, we further introduce RL-HIP, which calibrates event retention and pathway attributes using downstream forecasting feedback while keeping the forecasting model and pathway policy separately trained. We evaluate the framework on electricity-demand and Bitcoin forecasting with multiple numerical backbones. RL-HIP provides strong and consistent improvements for electricity demand, while the gains on Bitcoin are smaller and more architecture-dependent. These results suggest that the utility of external events in multi-step forecasting depends not only on their relevance, but also on how their predicted influence is represented and calibrated across future horizons.