用大模型解析时间序列关联，让预测过程透明可懂。
CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting
- 将时间序列转为叙事文本，通过大模型生成推理链
- 在真实数据集上预测精度媲美顶尖方法
- 适合需要解释性的金融、气象等场景
本文研究具有复杂依赖关系和非平稳动态的共演化时间序列预测问题，提出一种基于大语言模型（LLM）的新方法——上下文感知自回归语言模型（CAARL）。CAARL将时间序列分解为自回归片段，构建时序依赖图，并将其序列化为叙述文本，使大模型能够处理。该设计生成类似思维链的推理路径，中间步骤捕捉上下文动态并引导预测，实现透明化决策。通过显式推理轨迹关联预测结果，显著提升可解释性，同时保持高精度。在多个真实数据集上的实验验证了其有效性，表明CAARL是当前先进预测方法中兼具性能与可解释性的有力候选。
原文摘要 · Abstract (English)
In this paper we investigate forecasting coevolving time series that feature intricate dependencies and nonstationary dynamics by using an LLM Large Language Models approach We propose a novel modeling approach named ContextAware ARLLM CAARL that provides an interpretable framework to decode the contextual dynamics influencing changes in coevolving series CAARL decomposes time series into autoregressive segments constructs a temporal dependency graph and serializes this graph into a narrative to allow processing by LLM This design yields a chainofthoughtlike reasoning path where intermediate steps capture contextual dynamics and guide forecasts in a transparent manner By linking prediction to explicit reasoning traces CAARL enhances interpretability while maintaining accuracy Experiments on realworld datasets validate its effectiveness positioning CAARL as a competitive and interpretable alternative to stateoftheart forecasting methods
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。