FACTS-MAS：多智能体架构用于异构证据的自适应预测
FACTS-MAS: A Multi-Agent Architecture for Adaptive Forecasting from Heterogeneous Evidence
一篇多智能体预测的论文，把时间、季节性、事件等证据拆给不同智能体分别打分再加权，在 Zillow 和 FRED 数据上跑赢了 TimesFM。
FACTS-MAS 将预测证据拆分为时间动态、外部背景、季节性、结构信息和离散事件五类通道，由各智能体独立生成可检验的预测。框架通过防泄漏的历史验证测量每个智能体相对朴素基线的预测能力，并据此加权融合。在 Zillow 房源库存和 FRED 失业救济申请数据上，覆盖 15 个美国都会区、4/8/13 周三种预测窗口的测试中，该方法优于朴素基线和 TimesFM 基础模型。不过因子调整后的指数平滑模型表现与融合结果相当，说明该方法更适合作为证据检验与组合的系统化流程。
FACTS-MAS: A Multi-Agent Architecture for Adaptive Forecasting from Heterogeneous Evidence
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 https://github.com/Madumita24/FACTS-MAS.