[论文解读] EventCast: Hybrid Demand Forecasting in E-Commerce with LLM-Based Event Knowledge

Congcong Hu, Yuang Shi+5 more|arXiv (Cornell University)|Feb 7, 2026

Forecasting Techniques and Applications被引用 0

一句话总结

本论文提出一个面向电商的混合需求预测框架，利用基于大型语言模型的事件知识来提升预测。

ABSTRACT

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.

研究动机与目标

• 对改进电商需求预测的必要性进行动机阐述。
• 提出一种将 LLM 派生的事件知识与传统预测方法集成的混合方法。
• 演示如何将事件知识纳入电商的预测工作流程。
• 为利用 LLM 通过事件情境增强需求预测提供路径。

提出的方法

• 介绍一个将传统需求模型与 LLM 派生的事件知识相结合的混合预测框架。
• 从 LLM 中提取并整理与事件相关的知识以为预测提供信息。
• 将事件信号集成到预测流程中，在事件期间调整需求预测。
• 利用与信息抽取和企业应用相关的来源与技术。

实验结果

研究问题

• RQ1 如何将大型语言模型的事件知识以便于预测的方式表示？
• RQ2 将 LLM 派生的事件知识整合到电商需求预测中是否优于传统方法？
• RQ3 将事件信号与现有预测模型融合的最佳方式是什么？
• RQ4 在电商预测流程中部署基于 LLM 的事件知识有哪些实际考虑？

主要发现

• 在可用摘录中未提供定量结果。
• 工作集中在提出一种利用 LLM 基于事件知识进行电商需求预测的混合方法。
• 论文强调将事件信息整合到预测工作流程中。
• 它将贡献定位于电商需求预测、大型语言模型与信息抽取的交叉点。