Joint Forecasting of Extreme Events through Dual-Stage Cascade Reservoir Computing

Abstract

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.