Faculty of Science, Technology and Medicine
Faculty of Law, Economics and Finance
Faculty of Humanities, Education and Social Sciences
Interdisciplinary Centre for Security, Reliability and Trust
Luxembourg Centre for Systems Biomedicine
Luxembourg Centre for Contemporary and Digital History
Luxembourg Centre for European Law
Luxembourg Centre for Socio-Environmental Systems
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.
About the speaker
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.
Language
English
This is a free seminar. Registration is mandatory.
Supported by the Fond National de la Recherche
Luxembourg (19441346)