ERST: Learning Interaction from Entity Replacement in Spatiotemporal Forecasting

Spatiotemporal forecasting requires modeling interactions among multiple entities whose values change over time. Existing approaches obtain interaction information from entity representations, either by modeling relations among entities or by combining multiple entities into one representation. However, these approaches mainly use the relations or the resulting representation itself and do not directly measure how a representation formed from all entities changes with respect to one entity. We study this change by replacing the representation of one entity with a reference. We propose Entity Replacement for Spatiotemporal Forecasting (ERST), which replaces one entity representation while keeping the other entities, normalization, and function unchanged. ERST applies the same function before and after the replacement and uses the resulting difference as the response for that entity. The reference is constructed from the history of the entity and information from the other entities without directly using the current representation being replaced. The response therefore measures how the representation formed from all entities changes when one entity is replaced. We evaluate ERST on 12 datasets across five domains. ERST improves MAE in 23 of 24 combinations of forecasting architecture and dataset and matches the baseline in the remaining case.