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arXiv Machine Learning By Valentina Moretti, Cesare Alippi, Andrea Cini

Learning to Forecast on Multiple Spatiotemporal Hierarchies

Read the original on arXiv Machine Learning →

The paper introduces a graph deep learning framework that learns to forecast spatiotemporal data across multiple hierarchical structures, using variations among hierarchies as multi‑task regularization. By jointly training on different aggregation levels, the model reduces over‑specialization and improves forecasting accuracy. A novel top‑down operator ensures coherent forecasts with linear time and space complexity, making the approach computationally efficient.

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 Machine Learning.

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