FACTS-MAS: A Multi-Agent Architecture for Adaptive Forecasting from Heterogeneous Evidence

Authors: Madumita Karthikeyan , Umang Rajnikant Bid , Rong Pan

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Abstract: Real-world forecasting often requires integrating heterogeneous evidence whose predictive value varies across horizons, operating regimes, and domains. We introduce FACTS-MAS, a domain-agnostic multi-agent forecasting framework that decomposes evidence into specialized channels for temporal dynamics, exogenous context, seasonality, structural information, and discrete events. Each agent generates an independently testable forecast through a common interface, while leakage-safe historical validation measures its predictive skill relative to a naive reference. These skill estimates are used to weight and combine agent forecasts, so that uninformative evidence is either removed or downweighted rather than forced to participate. A calibration procedure selects domain-specific constant means based on historical validation folds. We evaluate FACTS-MAS on weekly for-sale housing inventory from Zillow and weekly unemployment insurance claims from FRED, for 15 U.S. metropolitan areas at 4-, 8-, and 13-week forecasting horizons. Our method outperforms both a naive last-value forecast method and the TimesFM foundation model. However, a factor-adjusted exponential smoothing model performs as well as the fused forecast. The evidence therefore supports FACTS-MAS as a systematic protocol for testing, calibrating, and recomposing evidence channels before assembling them into the strongest single model for a specific application domain. Code, data, and results are available at this https URL .

Comments:

29

Subjects:

Artificial Intelligence (cs.AI) ; Applications (stat.AP)

MSC classes:

86T42

ACM classes:

I.2.1

Cite as:

arXiv:2610.38225 [cs.AI]

(or

arXiv:2610.38225v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.38225

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arXiv-issued DOI via DataCite

Submission history

From: Rong Pan [ view email ]

[v1]

Wed, 28 Oct 2026 03:20:40 UTC (247 KB)

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