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

• URL: http://arxiv.org/abs/2510.11209v1
• Date: Mon, 13 Oct 2025 09:43:29 GMT
• Title: Cross-Scale Reservoir Computing for large spatio-temporal forecasting and modeling
• Authors: Nicola Alboré, Gabriele Di Antonio, Fabrizio Coccetti, Andrea Gabrielli,
• Abstract summary: We propose a new reservoir computing method for forecasting high-resolution datasets.<n>By combining multi-resolution inputs from coarser layers, our architecture better captures both local and global dynamics.<n>It outperforms standard parallel reservoir models in long-term forecasting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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