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 forecaster's joint predictive uncertainty current at test time through closed-form updates, not retraining. A fixed sensor graph supplies a low-dimensional spatial precision factor encoding which sensors' errors move together; at inference, Gaussian conditioning corrects The next forecast's mean and covariance from only the most recently observed residuals, and an exponential moving-average volatili...
| Authors | Seyed Mohamad Moghadas, Esther Rodrigo Bonet, Bruno Cornelis et al. |
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| Primary category | cs.LG |
| Published | 18 May 2026 |
| Last updated at source | 1 October 2026 |
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