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:
- domain-specialised forecasting agents for pattern recognition, anomaly detection, causal inference, and uncertainty quantification;
- a meta-cognitive layer for dynamic agent allocation;
- an emergent memory mechanism enabling cross-domain pattern transfer;
- multimodal contextual integration;
- 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.