ReasonCast: AI Framework for Agentic Demand Forecasting with Semantic Reasoning

A recent study published on arXiv presents ReasonCast, a novel framework for forecasting demand that combines textual event insights with time-series data. Cited as arXiv:2608.15291v1, the research introduces a structured semantic intervention strategy, allowing an agent to assess event context, no-text forecasts, and uncertainty to decide on the necessity of textual reasoning. Rather than using unstructured text, ReasonCast encapsulates event knowledge in organized fields that outline event significance, demand trends, and time frames. This approach seeks to enhance forecasting precision by selectively applying semantic reasoning to adjust future demand patterns, overcoming the shortcomings of current text-enhanced forecasting techniques. It is particularly relevant for retail, e-commerce, and supply chain scenarios, where events like promotions and holidays offer predictive insights. The full paper can be accessed at https://arxiv.org/abs/2608.15291.