{"slug": "macts-em-multi-agent-collaborative-time-series-forecasting-with-emergent-memory", "title": "MACTS-EM: Multi-Agent Collaborative Time Series Forecasting with Emergent Memory", "summary": "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.", "body_md": "arXiv:2610.02255v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/macts-em-multi-agent-collaborative-time-series-forecasting-with-emergent-memory", "canonical_source": "https://arxiv.org/abs/2610.02255", "published_at": "2026-10-05 04:00:00+00:00", "updated_at": "2026-10-05 04:12:34.360378+00:00", "lang": "en", "topics": ["machine-learning", "ai-agents", "ai-research", "artificial-intelligence"], "entities": ["MACTS-EM", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/macts-em-multi-agent-collaborative-time-series-forecasting-with-emergent-memory", "markdown": "https://wpnews.pro/news/macts-em-multi-agent-collaborative-time-series-forecasting-with-emergent-memory.md", "text": "https://wpnews.pro/news/macts-em-multi-agent-collaborative-time-series-forecasting-with-emergent-memory.txt", "jsonld": "https://wpnews.pro/news/macts-em-multi-agent-collaborative-time-series-forecasting-with-emergent-memory.jsonld"}}