MACTS-EM: Multi-Agent Collaborative Time Series Forecasting with Emergent Memory

A new arXiv paper (arXiv:2610.02255v1) introduces MACTS-EM, a multi-agent collaborative time series forecasting framework that combines domain-specialised forecasting agents, a meta-cognitive allocation layer, emergent memory for cross-domain pattern transfer, multimodal contextual integration, and adversarial robustness components. Evaluated across financial markets, climate patterns, energy consumption, and pandemic propagation, MACTS-EM reports 8-12% improvement in forecasting accuracy, 22-27% better zero-shot transfer, 16-21% enhanced resilience during regime shifts, and 15-18% faster recovery after distribution shifts. The authors conclude that collaborative, agentic approaches to time series forecasting represent a promising direction beyond traditional architectures for complex real-world scenarios requiring multi-resolution temporal understanding and contextual adaptation.

arXiv:2610.02255v1 Announce Type: new 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.