MACTS-EM：带涌现记忆的多智能体协作时间序列预测
原标题：MACTS-EM: Multi-Agent Collaborative Time Series Forecasting with Emergent Memory
AI 摘要
该论文提出 MACTS-EM 框架，将时间序列预测重构为多智能体协作过程，包含领域专用预测智能体、元认知层、涌现记忆机制、多模态上下文整合与对抗鲁棒性组件。在金融市场、气候、能源消耗和疫情传播等场景评估中，其预测准确率提升 8–12%，零样本迁移能力提升 22–27%，机制切换期间韧性提升 16–21%，分布偏移后恢复速度提升 15–18%。作者认为协作式智能体方法在复杂现实场景中优于传统单体架构。
正文节选
MACTS-EM: Multi-Agent Collaborative Time Series Forecasting with Emergent Memory Abstract Time series forecasting remains a critical challenge across numerous domains. Despite significant advancements, existing approaches struggle with complex phenomena such as regime shifts, cross-domain knowledge transfer, and multimodal data integration. This paper introduces Multi-Agent Collaborative Time Series Forecasting with Emergent Memory (MACTS-EM), a novel framework where specialised agents collabora