Cross-Scale Reservoir Computing for large spatio-temporal forecasting and modeling

Nicola Alboré; Gabriele Di Antonio; Fabrizio Coccetti; Andrea Gabrielli

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arXiv · 2510.11209

Cross-Scale Reservoir Computing for large spatio-temporal forecasting and modeling

Nicola Alboré·Gabriele Di Antonio·Fabrizio Coccetti·Andrea Gabrielli

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Abstract

We propose a new reservoir computing method for forecasting high-resolution spatiotemporal datasets. By combining multi-resolution inputs from coarser to finer layers, our architecture better captures both local and global dynamics. Applied to Sea Surface Temperature data, it outperforms standard parallel reservoir models in long-term forecasting, demonstrating the effectiveness of cross-layers coupling in improving predictive accuracy. Finally, we show that the optimal network dynamics in each layer become increasingly linear, revealing the slow modes propagated to subsequent layers.

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Nicola Alboré, Gabriele Di Antonio, Fabrizio Coccetti, Andrea Gabrielli. 2025-10-13. Cross-Scale Reservoir Computing for large spatio-temporal forecasting and modeling. https://arxiv.org/abs/2510.11209

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