Feb. 15, 2024, 5:44 a.m. | Jeroen Rombouts, Marie Ternes, Ines Wilms

stat.ML updates on arXiv.org arxiv.org

arXiv:2402.09033v1 Announce Type: cross
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 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 …

abstract aggregation arxiv businesses core decision decision making digital econ.em forecast machine machine learning making platform platforms stat.ap stat.me stat.ml temporal type

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