两篇新的研究论文提出了将大型语言模型（LLM）适配时间序列预测的新颖框架。第一个框架FM-LLM利用增强频率的专家混合方法，并结合傅里叶分析网络注入频谱动力学，在多个基准测试中取得了最先进的性能。第二个框架REATS采用LLM推理进行自适应集成学习，使用思维链生成可解释的、样本特定的集成权重，并优于竞争性基线。 AI
影响 这些新颖的框架可以通过利用大型语言模型的能力，显著提高时间序列预测模型的准确性和可解释性。
排序理由 arXiv上发表的两篇学术论文，详细介绍了将大型语言模型适配时间序列预测的新方法。
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arXiv:2608.11623v1 Announce Type: cross Abstract: Recent advances in Large Language Models (LLMs) have spurred cross-modal solutions for time-series forecasting. However, existing methods rely heavily on textual prompts for modality alignment-introducing nontrivial computational …
Recent advances in Large Language Models (LLMs) have spurred cross-modal solutions for time-series forecasting. However, existing methods rely heavily on textual prompts for modality alignment-introducing nontrivial computational overhead and failing to leverage the rich spectral…
arXiv:2608.10149v1 Announce Type: new Abstract: Due to the diversity of real-world time series, no single forecasting model consistently dominates across all samples. Ensemble learning addresses this by combining complementary model strengths, yet existing methods rely on fixed r…