SEMINAR OF THE THEMATIC PROJECT

Seminar of the Thematic Project (2023/01728-0) Econometric Modlling and Forecasting in High Dimensional Models (HDEM&For)

Speaker: Ines Wilms (Associate Professor at the Department of Quantitative Economics,Maastricht University)

Title: Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning

Abstract: Platform businesses operate on a digital core and their decision making requires highdimensional accurate forecast streams at different levels of cross-sectional (e.g., geographical regions) and temporal aggregation (e.g., minutes to days). It also necessitates coherent forecasts across all levels of the hierarchy to ensure aligned decision making across different planning units such as pricing, product, controlling and strategy. Given that platform data streams feature complex characteristics and interdependencies, we introduce a non-linear hierarchical forecast reconciliation method that produces cross-temporal reconciled forecasts in a direct and automated way through the use of popular machine learning methods. The method is sufficiently fast to allow forecast-based high-frequency decision making that platforms require. We empirically test our framework on a unique, large-scale streaming dataset from a leading on-demand delivery platform in Europe.

Type: Online