研究人员提出PAC-LLM框架，解决了混沌系统长期预测难题，在短期观测条件下也能准确预测。
PAC-LLM是一种基于大语言模型的混沌时间序列预测框架，能够在短期观测条件下进行长期预测。该模型通过相空间感知的自适应融合机制，结合相空间特征和文本信息，在多个混沌系统基准测试中表现优异。实验显示，PAC-LLM在短期和长期预测任务上均优于现有微调和零样本基线模型。
Predicting the Unpredictable: LLM-powered Long-term Chaotic Time Series Forecasting under Short-term Observations
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, which limits their applicability when only short-term observations are available. While recent Large Language Models (LLMs) have shown great potential for time series forecasting, their temporal representations are not explicitly tailored to the phase-space structure and nonlinear evolution of chaotic systems. To address these issues, we propose PAC-LLM, a phase-space-aware adaptive fusion framework for long-term chaotic time series forecasting powered by LLMs. PAC-LLM leverages learned phase-space features and textual information to fully enable LLM's time series forecasting capacity. In particular, we design an auxiliary feature module and a gated weighting mechanism for multivariate coupling information fusion and selection. Extensive experiments on representative chaotic systems demonstrate that our method outperforms existing fine-tuned and zero-shot baselines in both short-term and long-term predictions. Our ablation study further confirms the effectiveness of each key component in PAC-LLM.