ReasonCast: Agentic Demand Forecasting with Selective Semantic Reasoning
Researchers have developed ReasonCast, a new method for demand forecasting that combines historical sales data with future events and promotions. Unlike existing methods, ReasonCast explicitly distinguishes between relevant and irrelevant event information, using structured fields to represent the effects of each event on future dynamics. This approach is tested on various datasets, showing improved performance over traditional methods.
Researchers have developed ReasonCast, a new method for demand forecasting that combines historical sales data with future events and promotions. Unlike existing methods, ReasonCast explicitly distinguishes between relevant and irrelevant event information, using structured fields to represent the effects of each event on future dynamics. This approach is tested on various datasets, showing improved performance over traditional methods.
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Why it matters: This matters because demand forecasting is a critical task in many industries, including retail and manufacturing. Improving forecast accuracy can lead to better resource allocation, reduced waste, and increased customer satisfaction.
Source: https://arxiv.org/abs/2608.15291
This article was originally published at: https://arxiv.org/abs/2608.15291