混沌动力学中极端事件预测视界上的噪声诱导可预测性再分布
Noise-Induced Predictability Redistribution Across Forecast Horizons of Extreme Events in Chaotic Dynamics
- Petrozavodsk State University（彼得罗扎沃茨克国立大学）
- Industrial University of Ho Chi Minh City（胡志明市工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究噪声如何改变混沌系统中极端事件的可预测性，提出噪声诱导可预测性再分布（NIPR）现象，并通过实验验证噪声对短视界预测的增强作用。
AI中文摘要：
混沌动力学中的极端事件（EEs）是罕见的宽幅偏离，其可预测性可能受到动力学噪声的影响。我们研究在三阶自治混沌流中，噪声如何改变极端事件的发生及其在不同预测视界上的预测技能。对于所有实现，固定一个单一的干净数据阈值，宽事件定义为每次偏离的一个最大值，并使用直方图梯度提升（HistGradientBoosting）结合按时间顺序的数据分离，从15个时间单位的历史预测未来窗口W=15。随着观测历史与未来事件窗口之间的预测间隔G增加，干净的马修斯相关系数（MCC）从G=0时的0.641下降到G=15时的0.165。噪声依赖性通过八个振幅下的十对配对实现进行评估。平均短视界得分从干净数据中的0.456增加到sigma=0.007时的0.546；配对增益为0.0895（95%置信区间0.0494-0.1295；Holm校正p=0.0234）。噪声强烈增加极端事件的发生，而事件振幅和宽度保持相对稳定。在不同噪声水平下均衡正训练计数显著削弱了短视界增益，而强噪声降低了中间视界技能。我们将这种随视界变化的、不均匀的预测技能变化称为噪声诱导可预测性再分布（NIPR）。
英文摘要：
Extreme events (EEs) in chaotic dynamics are rare broad excursions whose forecastability can be altered by dynamical noise. We investigate how noise changes EE occurrence and prediction skill across forecast horizons in a third-order autonomous chaotic flow. A single clean-data threshold is frozen for all realizations, broad events are defined by one maximum per excursion, and a future window W=15 is predicted from a 15-time-unit history using HistGradientBoosting with chronological data separation. As the forecast gap G between the observed history and the future event window increases, the clean Matthews correlation coefficient (MCC) decreases from 0.641 at G=0 to 0.165 at G=15. Noise dependence is evaluated with ten paired realizations at eight amplitudes. The mean short-horizon score increases from 0.456 in clean data to 0.546 at sigma=0.007; the paired gain is 0.0895 (95% CI 0.0494-0.1295; Holm-adjusted p=0.0234). Noise strongly increases EE occurrence while event amplitude and width remain comparatively stable. Equalizing positive training counts across noise levels substantially attenuates the short-horizon gain, whereas strong noise reduces intermediate-horizon skill. We term this horizon-dependent, nonuniform change in forecast skill noise-induced predictability redistribution (NIPR).
