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arXiv AI By Seyed Mohamad Moghadas, Esther Rodrigo Bonet, Bruno Cornelis, Adrian Munteanu

Teger: Spatiotemporal Covariance for Probabilistic Traffic Forecasting

Read the original on arXiv AI →

The paper introduces TEGER, a residual covariance model that updates a forecaster’s joint predictive uncertainty at test time using closed‑form Gaussian conditioning on recent residuals and an exponential moving‑average volatility term. It leverages a fixed sensor graph to encode spatial correlations without retraining the underlying forecasting backbone. Experiments on four traffic datasets show significant improvements in 60‑minute CRPS, and applying TEGER to a frozen foundation model (Chronos) reduces CRPS from 0.1798 to 0.1736.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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