2025-04-25 –, Zeiss Plenary (Spectrum)
Time series forecasting in the retail industry is uniquely challenging: Datasets often include stockouts that censor actual demand, promotional events cause irregular demand spikes, new product launches face cold-start issues, and diverse demand patterns within an imbalanced product portfolio create modeling challenges.
In this talk, we’ll explore proven, real-world strategies and examples to address these problems. Learn how to successfully handle censored demand caused by stockouts, effectively incorporate promotional effects, and tackle the variability of diverse products using clustering and ensembling strategies. Whether you’re a seasoned data scientist or a Python developer exploring forecasting, the goal of this session is to introduce you to the key challenges in retail forecasting and equip you with actionable insights to successfully overcome them in real-life scenarios.
Retail time series forecasting is uniquely challenging: stockouts censor true demand, promotions cause irregular demand spikes, cold-start products lack historical data, and diverse product portfolios introduce modeling complexities. These challenges can lead to inefficiencies such as over- or understocking in the warehouses and therefore also to dissatisfied customers. This talk explores proven strategies to tackle these issues and deliver actionable insights.
Learn how to handle constrained demand caused by stockouts both with adequate imputation as well as machine learning strategies, incorporate promotional effects with suitable feature engineering techniques that also help in cases of incomplete promotional data, predict demand for new products using transfer learning and also discover how ensembling strategies and clustering can simplify forecasting for diverse, imbalanced datasets.
We’ll also highlight tools like statsforecast, neuralforecast, scikit-learn and our AutoML framework with a strong stacking ensembling mechanism in it's core. Whether you’re a seasoned data scientist or a Python developer exploring forecasting, the goal of this session is to introduce you to the key challenges in retail forecasting and equip you with actionable insights to successfully overcome them in real-life scenarios.
Intermediate
Expected audience expertise: Python:Novice
I earned both my Bachelor's and Master's degrees in Physics from the University of Heidelberg, specializing in Condensed Matter Physics and Computational Physics. During my Master's thesis in 2020, I advanced existing NLP Transformer architectures for timeseries applications where I worked extensively with uncertainty quantifications and normalizing flows. Since the beginning of 2021, I have been employed at Paretos, where the primary focus of my work lies in Timeseries Forecasting, specifically demand forecasting. Since 2023, Im leading the AI team at paretos which is giving me a good opportunity to combine my leadership skills with our super interesting research in scalable time series forecasting & optimization applications.