{"ID":5551592,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-04T15:29:24.262450661Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01082","arxiv_id":"2607.01082","title":"When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting","abstract":"Spatio-temporal point-process models must often generalise across space when local event histories are sparse. We study whether exogenous spatial context can compensate in such regimes. Using a fixed log-Gaussian Cox process backbone, we compare an event-only model with the same model augmented by AlphaEarth embeddings as linear spatial context. We evaluate spatial transfer on emergency medical services (EMS) forecasting across eight held-out regions, fixed forecast anchors, and a sweep over history length $w$, using only AlphaEarth (AE) embeddings available strictly before each anchor. AE improves out-of-region predictive performance across all history regimes, with the largest gains under scarce histories: approximately $2$--$6\\times$ multiplicative improvements at $1-2$ weeks, tapering to roughly $10$--$20\\%$ at $w=20$--$104$ weeks. These results show that contextual information can substantially stabilise spatially transferred point-process forecasts when event history is limited.","short_abstract":"Spatio-temporal point-process models must often generalise across space when local event histories are sparse. We study whether exogenous spatial context can compensate in such regimes. Using a fixed log-Gaussian Cox process backbone, we compare an event-only model with the same model augmented by AlphaEarth embeddings...","url_abs":"https://arxiv.org/abs/2607.01082","url_pdf":"https://arxiv.org/pdf/2607.01082v1","authors":"[\"Yahya Aalaila\",\"Mouad Elhamdi\",\"Gerrit Großmann\",\"Daniel Jenson\",\"Elizaveta Semenova\",\"Sebastian Vollmer\"]","published":"2026-07-01T15:38:39Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}