# Nexus：一个用于时间序列预测的多智能体框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-14 08:00
- AIHOT 分数：64
- AIHOT 链接：https://aihot.virxact.com/items/cmp6bj8c200icslnzsemx4lz2
- 原文链接：https://arxiv.org/abs/2605.14389

## AI 摘要

时间序列预测需结合数值模式与上下文信息如新闻。研究团队推出Nexus框架，它将预测分解为多阶段：分离宏观与微观时序波动，整合非结构化上下文信息，再综合生成预测。该框架表明，当前大语言模型具备比以往认知更强的内在预测能力，关键在于组织数值与上下文推理。在知识截止日期后的Zillow房地产和波动股市数据上评估，Nexus持续匹配或超越了最先进的时序基础模型及大语言模型基线。此外，Nexus能生成高质量推理轨迹，明确展示预测背后的核心驱动因素，推动预测向代理推理问题发展。

## 正文

Time series forecasting is not just numerical extrapolation, but often requires reasoning with unstructured contextual data such as news or events. While specialized Time Series Foundation Models (TSFMs) excel at forecasting based on numerical patterns, they remain unaware to real-world textual signals. Conversely, while LLMs are emerging as zero-shot forecasters, their performance remains uneven across domains and contextual grounding. To bridge this gap, we introduce Nexus, a multi-agent forecasting framework that decomposes prediction into specialized stages: isolating macro-level and micro-level temporal fluctuations, and integrating contextual information when available before synthesizing a final forecast. This decomposition enables Nexus to adapt from seasonal signals to volatile, event-driven information without relying on external statistical anchors or monolithic prompting. We show that current-generation LLMs possess substantially stronger intrinsic forecasting ability than previously recognized, depending critically on how numerical and contextual reasoning are organized. Evaluated on data strictly succeeding LLM knowledge cutoffs spanning Zillow real estate metrics and volatile stock market equities, Nexus consistently matches or outperforms state-of-the-art TSFMs and strong LLM baselines. Beyond numerical accuracy, Nexus produces high-quality reasoning traces that explicitly show the fundamental drivers behind each forecast. Our results establish that real-world forecasting is an agentic reasoning problem extending well beyond only sequence modeling.