{"ID":2856240,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.11209","arxiv_id":"2510.11209","title":"Cross-Scale Reservoir Computing for large spatio-temporal forecasting and modeling","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.","short_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 mod...","url_abs":"https://arxiv.org/abs/2510.11209","url_pdf":"https://arxiv.org/pdf/2510.11209v1","authors":"[\"Nicola Alboré\",\"Gabriele Di Antonio\",\"Fabrizio Coccetti\",\"Andrea Gabrielli\"]","published":"2025-10-13T09:43:29Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"physics.comp-ph\"]","methods":"[]","has_code":false}