Cross-temporal forecast reconciliation at digital platforms with machine learning

Jeroen Rombouts, Marie Ternes and Ines Wilms

International Journal of Forecasting, 2025, vol. 41, issue 1, 321-344

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.

Keywords: Hierarchical time series; Forecast reconciliation; Machine learning; Cross-temporal aggregation; Demand forecasting; Platform econometrics (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:41:y:2025:i:1:p:321-344

DOI: 10.1016/j.ijforecast.2024.05.008

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