Probabilistic Demand Forecasting at Scale

Probabilistic Demand Forecasting at Scale

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Scaling demand forecasting under uncertainty to millions of time series - Hagop Dippel

About this event


  • Introduction to demand forecasting in the context of e-commerce logistics.
  • Review of main probabilistic forecasting techniques.
  • How can probabilistic demand forecasting enable stochastic downstream applications (human resource planning, inventory optimisation)
  • Scalable training and inference system which can handle thousands/millions of time series with a cold-start problem.

About the speaker:

Hagop Dippel is an Applied Scientist at Zalando, where he focuses on building demand forecasting and inventory optimisation applications. He particularly enjoys bringing research ideas to end-2-end production systems. He’s passionate about deep learning applied to real-world industry use cases and human centred AI. Hagop studied Data Science and Econometrics at the Aix-Marseille University in France. In his free time, you can find him cycling or running (just contact him if you want to jog and/or chat!)

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