English(EN) Predicting the Unpredictable: LLM-powered Long-term Chaotic Time Series Forecasting under Short-term Observations

新的PAC-LLM框架利用LLM增强混沌时间序列预测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了PAC-LLM，一个旨在改进混沌时间序列长期预测的新框架，尤其是在只有短期数据可用的情况下。该方法将大型语言模型（LLMs）与相空间特征和文本信息相结合，以更好地捕捉混沌系统的非线性动力学。实验表明，PAC-LLM在基准混沌系统上的短期和长期预测方面均优于现有方法。 AI

影响这项研究可能有助于提高处理混沌系统的领域（如气候建模或金融市场）的长期预测准确性。

排序理由该集群包含一篇详细介绍时间序列预测新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Yuhang Yao, Bohan Jiang · 2026-09-01 04:00

预测不可预测：LLM驱动的短期观测下的长期混沌时间序列预测

arXiv:2608.29579v1 Announce Type: new Abstract: Chaotic time series forecasting is a challenging task due to its sensitivity to initial conditions and long-term unpredictability. Traditional methods typically rely on sufficient temporal trajectories to learn long-term dynamics, w…