English(EN) Adaptive Nonlinear Vector Autoregression: Robust Forecasting for Noisy Chaotic Time Series

新型自适应模型增强混沌时间序列预测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一种新的自适应非线性向量自回归（NVAR）模型，该模型能够改进对噪声和混沌时间序列数据的预测。该模型将线性输入与可训练多层感知器（MLP）生成的特征相结合，使其能够学习数据驱动的非线性。与传统的NVAR和水库计算方法不同，这种自适应方法通过基于梯度的优化进行联合训练，提高了可扩展性和预测精度，尤其是在噪声条件下。实验表明，该模型在Lorenz-63模型和厄尔尼诺-南方涛动等混沌系统上优于标准的NVAR和回声状态网络。 AI

影响为复杂、嘈杂的数据集提供了改进的预测能力，可能使气候科学和经济学等领域受益。

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arXiv cs.AI TIER_1 English(EN) · Sherkhon Azimov, Susana Lopez-Moreno, Eric Dolores-Cuenca, Sieun Lee, Jae-Il Kwon, Sangil Kim · 2026-09-10 04:00

自适应非线性向量自回归：针对混沌噪声时间序列的鲁棒预测

arXiv:2507.08738v3 Announce Type: replace-cross Abstract: Nonlinear vector autoregression (NVAR) and reservoir computing (RC) have shown promise in forecasting chaotic dynamical systems, such as the Lorenz-63 model and El Nino-Southern Oscillation. However, their reliance on fixe…

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自适应非线性向量自回归：针对混沌噪声时间序列的鲁棒预测

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