Demand forecasting is a cornerstone of e-commerce operations, directly impacting inventory planning and fulfillment scheduling. However, existing forecasting systems often fail during high-impact periods such as flash sales, holiday campaigns, and sudden policy interventions, where demand patterns shift abruptly and unpredictably. In this paper, we introduce EventCast, a modular forecasting framework that integrates future event knowledge into time-series prediction. Unlike prior approaches that ignore future interventions or directly use large language models (LLMs) for numerical forecasting, EventCast leverages LLMs solely for event-driven reasoning. Unstructured business data, which covers campaigns, holiday schedules, and seller incentives, from existing operational databases, is processed by an LLM that converts it into interpretable textual summaries leveraging world knowledge for cultural nuances and novel event combinations. These summaries are fused with historical demand features within a dual-tower architecture, enabling accurate, explainable, and scalable forecasts. Deployed on real-world e-commerce scenarios spanning 4 countries of 160 regions over 10 months, EventCast achieves up to 86.9% and 97.7% improvement on MAE and MSE compared to the variant without event knowledge, and reduces MAE by up to 57.0% and MSE by 83.3% versus the best industrial baseline during event-driven periods. EventCast has deployed into real-world industrial pipelines since March 2025, offering a practical solution for improving operational decision-making in dynamic e-commerce environments.

翻译：需求预测是电子商务运营的基石，直接影响库存规划和履约调度。然而，现有预测系统在高影响时期（如闪购、假日促销和突发政策干预）往往失效，这些时期的需求模式会发生突然且不可预测的转变。本文提出EventCast，一个将未来事件知识整合到时间序列预测中的模块化预测框架。与以往忽略未来干预或直接使用大语言模型进行数值预测的方法不同，EventCast仅利用LLM进行事件驱动的推理。来自现有运营数据库的非结构化业务数据（涵盖促销活动、假日安排和卖家激励）由LLM处理，LLM利用世界知识处理文化差异和新颖事件组合，将其转换为可解释的文本摘要。这些摘要通过双塔架构与历史需求特征融合，从而实现准确、可解释且可扩展的预测。在跨越4个国家160个区域、为期10个月的真实电子商务场景中部署，EventCast相比无事件知识的变体，在MAE和MSE上分别实现了高达86.9%和97.7%的提升；在事件驱动时期，相比最佳工业基线，MAE降低了高达57.0%，MSE降低了83.3%。EventCast自2025年3月起已部署到真实的工业流水线中，为动态电子商务环境中改进运营决策提供了一个实用的解决方案。