Jeroen Rombouts, Marie Ternes, Ines Wilms
arXiv 14 Feb 2024 · Econometrics · publishedInternational Journal of Forecasting (2024) · 11 citations (OpenAlex)
arXiv:2402.09033 · PDF · DOI · OpenAlex · Extracted main text
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 method that produces cross-temporal reconciled forecasts in a direct and automated way through the use of 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., and Panagiotelis… (2024) Forecast reconciliation: A review | 0.928 | 4 | 3 | 100% |
| 2 | Spiliotis, E., Abolghasemi, M., Hyndman, R. J., Petropoulos, F., and… (2021) Hierarchical forecast reconciliation with machine learning | 0.920 | 9 | 4 | 78% |
| 3 | Di Fonzo, T. and Girolimetto, D (2023) Cross-temporal forecast reconciliation: Optimal combination method and heuristic alternatives | 0.874 | 5 | 2 | 100% |
| 4 | Makridakis, S., Spiliotis, E., and Assimakopoulos, V (2022) M5 accuracy competition: Results, findings, and conclusions | 0.737 | 3 | 2 | 100% |
| 5 | Kourentzes, N. and Athanasopoulos, G (2019) Cross-temporal coherent forecasts for Australian tourism | 0.644 | 2 | 2 | 100% |
| 6 | Januschowski, T., Wang, Y., Torkkola, K., Erkkilä, T., Hasson, H., a… (2022) Forecasting with trees | 0.511 | 2 | 1 | 100% |
| 7 | Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., and Panagiotelis… (2024) Editorial: Innovations in hierarchical forecasting | 0.405 | 1 | 1 | 100% |
| 8 | Di Fonzo, T. and Girolimetto, D (2024) Forecast combination-based forecast reconciliation: Insights and extensions | 0.405 | 1 | 1 | 100% |
| 9 | Girolimetto, D. and Di Fonzo, T (2023) FoReco: Point Forecast Reconciliation | 0.405 | 1 | 1 | 100% |
| 10 | Girolimetto, D., Athanasopoulos, G., Di Fonzo, T., and Hyndman, R. J (2023) Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 56 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions | 0.405 | 1 | 1 |