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Cross-temporal forecast reconciliation at digital platforms with machine learning

Author

Listed:
  • Rombouts, Jeroen
  • Ternes, Marie
  • Wilms, Ines

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.

Suggested Citation

  • Rombouts, Jeroen & Ternes, Marie & Wilms, Ines, 2025. "Cross-temporal forecast reconciliation at digital platforms with machine learning," International Journal of Forecasting, Elsevier, vol. 41(1), pages 321-344.
    • Handle: RePEc:eee:intfor:v:41:y:2025:i:1:p:321-344
    DOI: 10.1016/j.ijforecast.2024.05.008
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