EventCast: Hybrid Demand Forecasting in E-Commerce with LLM-Based Event Knowledge
Congcong Hu, Yuang Shi, Fan Huang, Yang Xiang, Zhou Ye, Ming Jin, Shiyu Wang

TL;DR
EventCast is a novel demand forecasting framework that integrates future event knowledge via LLMs into time-series models, significantly improving accuracy during high-impact periods in e-commerce.
Contribution
It introduces a modular approach that leverages LLMs for event reasoning and fuses this with historical data for more accurate, explainable demand forecasts during unpredictable events.
Findings
Achieves up to 86.9% improvement in MAE over variants without event knowledge.
Reduces MAE by up to 57.0% during event-driven periods.
Successfully deployed in real-world e-commerce pipelines since March 2025.
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…
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Taxonomy
TopicsForecasting Techniques and Applications · Stock Market Forecasting Methods · Traffic Prediction and Management Techniques
