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This paper critically audits benchmark datasets and baselines for spatiotemporal forecasting, showing that classical linear models often compete with graph neural networks, highlighting issues with discriminative reliability and advocating for more rigorous evaluation.
Otter Weather is a computationally efficient AI model for medium-range weather forecasting that outperforms numerical weather prediction baselines and frontier AI models while requiring significantly less training compute, aiming to democratize high-performance weather prediction.
This paper proposes a nested spatiotemporal forecasting framework that uses spectral clustering to construct semantically coherent macro-level regions, which provide top-down guidance for fine-grained micro-level predictions. Experiments on high-dimensional datasets show consistent improvements over state-of-the-art baselines.