[Submitted on 30 Sep 2026]
Title:MACTS-EM: Multi-Agent Collaborative Time Series Forecasting with Emergent Memory
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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 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.
Comments: 16 pages, 3 figures, 5 tables
Subjects:
Machine Learning (cs.LG); Multiagent Systems (cs.MA)
Cite as: arXiv:2610.02255 [cs.LG]
(or arXiv:2610.02255v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.02255
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ahmad Shahi [view email] [v1] Wed, 30 Sep 2026 19:30:32 UTC (615 KB)
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