Optimizing Retail Forecasting Through AI Models
summary
Claims that generative AI speeds proof‑of‑concepts and code generation for retail forecasting, producing Python scripts (pandas, statsmodels, matplotlib), EDA visualizations and pipelines that handle missing values and backtests, plus signal extraction from headlines and social media to merge with time series. Demonstrates techniques like chain of thought and self-refined prompting and explores tree of thought for model selection, warns performance hinges on prompt clarity and data quality and shows AI agents automating demand sensing and reorder recommendations.
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chapters (9)
- 0:00Introduction to Retail Forecasting and AI
- 1:52Why Forecasting is Essential in Retail
- 7:23Challenges in Retail Forecasting
- 11:15Introduction to Prompt Engineering
- 13:12Techniques in Prompt Engineering
- 20:17Applications of Prompt Engineering in Forecasting
- 30:25Practical Implementation and Considerations
- 33:53Future Trends in AI and Retail Forecasting
- 37:29Conclusion and Contact Information
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