ReasonCast framework integrates semantic reasoning for improved demand forecasting

Researchers have developed ReasonCast, a novel framework for demand forecasting that integrates textual event knowledge with numerical time-series data. This system uses an agent to selectively apply semantic reasoning, translating event details into structured fields that modify forecast dynamics. The framework employs a post-training curriculum, including Schema SFT and forecast-utility RL, to align reasoning with marginal forecast improvements, demonstrating a reduction in WMAPE across various sensitive categories. AI

IMPACT Introduces a novel agentic approach to integrate textual context into time-series forecasting, potentially improving accuracy in event-sensitive domains.

RANK_REASON Academic paper detailing a new method for demand forecasting. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• Apple M5 chip
• arXiv
• CatalyzeX
• DagsHub
• forecast-utility RL
• Gotit.pub
• Hugging Face
• ReasonCast
• Schema SFT
• ScienceCast
• semantic-field RL
• WMAPE

• paper
• model release

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