CAME：面向可解释的季度前瞻营收预测的公司感知证据-记忆专家模型
CAME: Company-Aware Evidence-Memory Experts for Interpretable Quarter-Ahead Revenue Forecasting
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中文总结 AI 辅助
CAME通过残差预测框架结合无泄漏统计锚点与语义证据，在336个公司-季度回测中实现最低点估计误差，优于历史+指引方法，并提供可解释的证据卡与记忆痕迹。
中文摘要 AI 辅助
季度前瞻营收预测需要公司级别的数值精度、严格的时间有效性和对叙述性披露的公司特定解释。大型语言模型（LLM）能够提炼文本证据，但可能产生尺度不一致的预测，而基于历史的锚点虽稳定却遗漏了预测时点的信号，如产品转型、供应链约束和管理层指引。我们提出CAME（公司感知证据-记忆专家模型），一种残差预测框架，在当前语义证据和先前误差模式证明调整合理时，对无泄漏统计锚点进行细化。在对来自12家大型上市科技和平台公司的336个公司-季度进行的发展性包含滚动回测中，CAME在报告的方法中实现了最低的总体点估计误差，相对于匹配的统计锚点具有统计支持的宏观sMAPE改进，并在所有六项总体指标上优于历史+指引方法。CAME还将调整与来源链接的证据卡和受保护的记忆痕迹相关联，支持预测检查、来源追溯和失败定位。
英文摘要
Quarter-ahead revenue forecasting requires company-scale numerical accuracy, strict temporal validity, and company-specific interpretation of narrative disclosures. LLMs can distill textual evidence but can produce scale-misaligned forecasts, whereas history-based anchors are stable but miss forecast-time signals such as product transitions, supply constraints, and management guidance. We introduce CAME (Company-Aware Evidence-Memory Experts), a residual-forecasting framework that refines a no-leakage statistical anchor when current semantic evidence and prior error patterns justify an adjustment. On a development-inclusive rolling backtest of 336 company-quarters from 12 large public technology and platform firms, CAME achieves the lowest aggregate point-estimate error among the reported methods, with statistically supported macro-sMAPE gains over the matched Statistical Anchor, and outperforms History + Guidance on all six aggregate metrics. CAME also links adjustments to source-linked evidence cards and guarded memory traces, supporting forecast inspection, provenance, and failure localization.
