发表机构
State Key Laboratory of Integrated Service Networks, School of Communications Engineering, Xidian University（西安电子科技大学通信工程学院集成服务网络国家重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出双级级联储层计算框架，结合传统RC与NGRC分支，先分类后回归，实现极端事件时间与峰值强度的联合定量预测，在模拟VCSEL数据上SEDI>0.8，MAE约0.2ns和0.2a.u.，支持20ns预警时域。
AI 中文摘要
储层计算（RC）为预测极端事件（EEs）提供了一种高效的数据驱动方法，这些事件对应于罕见且大幅值的动力学现象。我们提出了一种双级级联框架，用于联合预测即将发生的极端事件的时间与峰值强度。传统RC分支整合长期前兆动力学以支持稳定检测和长时域预测，而NGRC分支则捕获局部非线性波形几何形状，以改善精细的到达峰值时间定位并补充峰值强度估计。融合特征随后输入岭分类器，在检测到前兆时发出二元警报。仅在此之后，两个基于真阳性快照训练的岭回归器才估计到达峰值时间和峰值强度。这种先分类后回归的设计在无需数据重采样的情况下解决了严重的类别不平衡问题。在模拟泵浦调制VCSEL数据上的评估表明，混合模型实现了大于0.8的SEDI值，MAE约为0.2纳秒和0.2任意单位，在长达20纳秒的预警时域内保持性能。该框架将极端事件预测从二元警报推进到完全定量的双目标预测。
英文摘要
Reservoir computing (RC) offers an efficient data-driven approach for forecasting extreme events (EEs), which correspond to rare and large-amplitude dynamical occurrences. We propose a dual-stage cascade framework that jointly predicts both the timing and peak intensity of upcoming EEs. A traditional RC branch integrates long-term precursor dynamics to support stable detection and long-horizon prediction, while an NGRC branch captures local nonlinear waveform geometry to improve fine-grained time-to-peak localization and complement peak-intensity estimation. The fused features then feed a ridge classifier that issues a binary alarm upon detecting precursors. Only then do two ridge regressors, trained on true-positive snapshots, estimate time-to-peak and peak intensity. This classify-then-regress design addresses severe class imbalance without data resampling. Evaluated on simulated pump-modulated VCSEL data, the hybrid model achieves a SEDI value >0.8, with MAEs around 0.2 ns and 0.2 a. u., maintaining performance up to a 20 ns warning horizon. The framework advances extreme-event forecasting from binary warnings to fully quantitative dual-objective prediction.
