When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting
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Computer Science > Machine Learning
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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.01082 [cs.LG] |
| (or arXiv:2607.01082v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.01082
arXiv-issued DOI via DataCite (pending registration)
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