跨尺度残差计算提升高分辨率时空预测精度
Cross-Scale Reservoir Computing for large spatio-temporal forecasting and modeling
- 多尺度输入从粗到细融合，捕捉局部与全局动态
- 在海表温度数据上长期预测优于传统并行模型
- 各层最优动态趋近线性，揭示慢模式传递机制
我们提出一种新型的残差计算方法，用于高分辨率时空数据的预测。通过融合从粗到细的多尺度输入，该架构能更好地捕捉局部与全局动态。应用于海表温度数据时，其在长期预测中优于标准并行残差模型，证明了跨层耦合对提升预测准确性的有效性。最后，我们发现每一层的最优网络动态逐渐趋于线性，揭示了慢模式向后续层传播的机制。
原文摘要 · Abstract (English)
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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