Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917680086253568 |
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| author | Rombouts, Jeroen Ternes, Marie Wilms, Ines |
| author_facet | Rombouts, Jeroen Ternes, Marie Wilms, Ines |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09033 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning Rombouts, Jeroen Ternes, Marie Wilms, Ines Econometrics Applications Methodology Machine Learning 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. |
| title | Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning |
| topic | Econometrics Applications Methodology Machine Learning |
| url | https://arxiv.org/abs/2402.09033 |