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arXiv Machine Learning By Ahmad Shahi, Mamehgol Yousefi

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

Read the original on arXiv Machine Learning →

MACTS-EM is a new multi‑agent framework for time series forecasting that combines domain‑specialised agents, a meta‑cognitive allocation layer, emergent memory for cross‑domain transfer, multimodal context integration, and adversarial robustness. The authors evaluate the system on financial, climate, energy, and pandemic data, reporting 8‑12% higher accuracy, 22‑27% better zero‑shot transfer, 16‑21% greater resilience to regime shifts, and 15‑18% faster recovery from distribution changes compared to existing methods. These results suggest that collaborative, agent‑based approaches can outperform traditional architectures in complex, real‑world forecasting tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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