In this video, we break down the data-driven approach to forecasting retail beef prices, highlighting how time-series and machine-learning methods can capture seasonality, demand shifts, and lagged effects. We also explore why prices often change around key consumer moments—like the weeks following July 4th—and what those patterns imply about the balance between supply and demand in the grocery market.

We’re joined by David Anderson, PhD., from Texas A&M University, who explains the motivation behind the research and the practical goal of building more accurate predictions for everyday food costs. From the role of protein substitution and value cuts to the way broader economic signals can indirectly influence grocery choices, this discussion connects the modeling techniques to real shopper behavior—making the forecasting feel both understandable and actionable.