Teger: Spatiotemporal Covariance for Probabilistic Traffic Forecasting

Traffic conditions drift -- demand patterns, incident dynamics, and sensor behavior shift over a deployment's lifetime -- so a joint uncertainty estimate fit once at training time and left static will miscalibrate as conditions change. We present TEGER, a residual covariance model that keeps a forec