ML-Based Time Series Regression - Concepts from Demand Forecasting

Explore machine learning-based time series regression concepts through the lens of demand forecasting in this insightful conference talk. Delve into the challenges of integrating AI technology into core business processes, focusing on replenishment and pricing in the retail sector. Learn how machine learning applications can optimize order quantities and shape demand through price settings. Discover the importance of accurate demand forecasts in supporting business decisions, and understand why predicting full probability distributions is crucial for optimization. Examine how machine learning's ability to consider multiple influencing variables and generalize patterns makes it ideal for demand forecasting. Gain insights into overcoming human bias in decision-making under uncertainty and the importance of automating predictions and decisions at scale in retail. This talk, presented by Felix Wick, GVP of Data Science at Blue Yonder, offers valuable knowledge for those interested in applying machine learning to time series regression and demand forecasting in business contexts.

ML Based Time Series Regression Concepts that can be Learned from Demand Forecasting