Not All Events Are Equal: Capturing Heterogeneous Impact of Aperiodic Events in Long-Term Spatio-Temporal Forecasting

Aperiodic events, such as traffic incidents and extreme weather, induce irregular deviations from recurring spatio-temporal patterns. Accurately forecasting these deviations is particularly challenging over long horizons. Existing methods typically approach this challenge by decomposing signals into regular and aperiodic components, but usually process all aperiodic components with an event-invariant parameterization. This overlooks the fact that the impact of an aperiodic event is heterogeneous, depending on when and where the event occurs and what type it is. A event-invariant model, agnostic to this heterogeneity, cannot adequately capture it. To address this challenge, we propose HEAM (Heterogeneous Event-Adaptive Modulation), a plug-and-play module that can be integrated into existing forecasting backbones to achieve event-adaptive modeling of the aperiodic component instead of event-invariant modeling.Specifically, we propose a Decompose-Situate-Identify pipeline, that generates modulation vectors from a library of modulator pools, conditioned on the spatio-temporal context and the latent event type of the aperiodic component. These vectors then modulate the aperiodic representation, so that the heterogeneous impact is explicitly captured.Experiments on datasets spanning multiple domains show that HEAM improves strong backbones of different architectural families, on average across all datasets and horizons. The gains are largest at the longest forecast horizon and in event-dense periods, particularly where the modeling of the heterogeneous impact of aperiodic events matters most.