Adapting to Rare Regimes without Forgetting: Layered Neuron Plasticity for Spatiotemporal Forecasting

Known, observable regime shifts, including public holidays and severe weather, can cause the largest errors of an otherwise accurate spatiotemporal forecaster. Adapting a deployed model with only a few rare-regime labels creates a stability–plasticity problem: unrestricted fine-tuning can degrade routine forecasts, whereas a localized update may remain poorly coordinated with the rest of the network. We introduce Layered Neuron Plasticity (LNP), a detect-update-restore framework for this setting. LNP identifies a rare-regime-responsive parameter subspace by cross-example consensus, updates only that subspace, and restores coordination by re-optimizing its complement on the full training set while retaining the original predictor as an event-aware fallback. A local second-order analysis motivates limiting update support and magnitude. Across six dataset-regime settings with ten paired seeds, LNP reduces mean rare-window MAE in every setting while maintaining or improving mean general- and all-window performance. LNP therefore provides a reliability-oriented approach to few-label adaptation for known, observable regime shifts, where rare-event gains must not come at the expense of routine forecasts.