A hands-on forecasting guide: from theory to practice
Ian Spektor, Diego Kiedanski, Mathieu Guillame-Bert
Forecasting is central to decision-making in virtually all technical domains. For instance, predicting product sales in retail, forecasting energy demand, and anticipating customer churn all have tremendous value across different industries. However, the landscape of forecasting techniques is as diverse as it is useful, and different techniques and expertise are adapted to different types and sizes of data.
In this hands-on workshop, we give an overview of forecasting concepts, popular methods, and practical considerations. We’ll walk you through data exploration, data preparation, feature engineering, statistical forecasting (e.g., STL, ARIMA, ETS), forecasting with tabular machine learning models (e.g., decision forests), forecasting with deep learning methods (e.g., TimesFM, DeepAR), meta-modeling (e.g., hierarchical reconciliation and relational modeling, ensembles, resource models), and how to safely evaluate such temporal models.