DEM Lunch Seminar: Ines WILMS, Maastricht University, NL

We are pleased to invite you to a Lunch seminar on " Cross-temporal forecast Reconciliation at digital platforms with machine learning" .

Abstract

Platform businesses operate on a digital core, and their decision-making requires high-dimensional 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 hierarchy levels 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 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 unique, large-scale streaming datasets from a leading on-demand delivery platform in Europe and a bicycle-sharing system in New York City.

Ines Wilms is an Associate Professor at the School of Business and Economics of Maastricht University. Her research develops flexible statistical learning methods and software for analysing big, complex time series datasets across a variety of fields including marketing, finance and macro-economics. She received a Ph.D. (Business Economics) from KU Leuven, and a M.S. and B.S (Business Economics) from KU Leuven.

The event is supported by the Luxembourg National Research Fund (FNR) 19441346