arXiv Machine Learning
Oct 16

Learning to Forecast on Multiple Spatiotemporal Hierarchies

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.

By Valentina Moretti, Cesare Alippi, Andrea Cini