Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 18·5 min read
ReasonCast: Agentic Demand Forecasting with Selective Semantic Reasoning
TL;DR
ReasonCast is a novel framework for demand forecasting that integrates structured semantic reasoning with time-series data, achieving significant accuracy improvements. It reduces WMAPE by up to 3.29 percentage points in holiday-sensitive categories.
✦ Why It Matters
Implement ReasonCast to enhance your demand forecasting accuracy today.
Key Takeaways
- Utilize structured semantic fields for event knowledge representation.
- Implement selective reasoning to improve forecast accuracy.
- Monitor WMAPE changes to assess intervention effectiveness.
- Avoid unnecessary semantic interventions during stable sales periods.
How It Works
ReasonCast operates by using an agent that evaluates the relevance of event context and forecast uncertainty. It employs structured fields to capture key aspects of events, such as demand direction and peak intensity, which interact with time-series data.
This dual-path approach allows for both additive corrections to trends and multiplicative adjustments for event-driven shifts, enhancing the overall forecasting model.