发表机构
Unitec Institute of Technology（Unitec理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有时间序列预测方法在机制转换、跨域迁移和多模态集成上的不足，提出MACTS-EM多智能体协作框架，通过涌现记忆与元认知分配，在多个领域实现8-12%准确率提升及显著更强的零样本迁移和鲁棒性。
AI 中文摘要
时间序列预测在众多领域中仍然是一个关键挑战。尽管取得了显著进展，现有方法在处理诸如机制转换、跨领域知识迁移和多模态数据集成等复杂现象时仍存在困难。本文介绍了具有涌现记忆的多智能体协作时间序列预测（MACTS-EM），这是一个新颖的框架，其中专门化的智能体协作以实现卓越的预测性能。MACTS-EM架构整合了：（1）用于模式识别、异常检测、因果推断和不确定性量化的领域专门化预测智能体；（2）用于动态智能体分配的元认知层；（3）实现跨领域模式迁移的涌现记忆机制；（4）多模态上下文集成；以及（5）对抗性鲁棒性组件。在金融市场、气候模式、能源消耗和疫情传播上的评估表明，MACTS-EM在大多数场景中优于现有方法，预测准确率提高8-12%，零样本迁移能力提升22-27%，机制转换期间的韧性增强16-21%，分布转换后的恢复速度加快15-18%。我们的研究结果表明，协作式、智能体化的时间序列预测方法代表了超越传统架构的一个有前景的方向，特别是在需要多分辨率时间理解和上下文适应的复杂现实世界场景中。
英文摘要
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 collaborate to achieve superior forecasting performance. The MACTS-EM architecture integrates: (1) domain-specialised forecasting agents for pattern recognition, anomaly detection, causal inference, and uncertainty quantification; (2) a meta-cognitive layer for dynamic agent allocation; (3) an emergent memory mechanism enabling cross-domain pattern transfer; (4) multimodal contextual integration; and (5) adversarial robustness components. Evaluation across financial markets, climate patterns, energy consumption, and pandemic propagation demonstrates that MACTS-EM outperforms existing approaches in most scenarios, with 8-12% improvement in forecasting accuracy, 22-27% better zero-shot transfer capability, 16-21% enhanced resilience during regime shifts, and 15-18% faster recovery after distribution shifts. Our findings suggest that collaborative, agentic approaches to time series forecasting represent a promising direction beyond traditional architectures, particularly for complex real-world scenarios requiring multi-resolution temporal understanding and contextual adaptation.
Comments16 pages, 3 figures, 5 tables
