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Coherent Hierarchical Forecasting for Proportion and Discrete Time Series — arXiv:2609.17918

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.17918
License (as archived): CC0-1.0
Archived: 2026-10-15T15:39:18.040Z
SHA-256 of the archived text: 112bdac9f505aeb0065c547704d800826ba338ae659f68607377589d054db0f7
Stable URL of this page: https://metraipolis.com/documento/dc_271bed5656cc

How to cite

Coherent Hierarchical Forecasting for Proportion and Discrete Time Series — arXiv:2609.17918 — arXiv (en), https://arxiv.org/abs/2609.17918 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_271bed5656cc (Metraipolis archive dc_271bed5656cc, sha256 112bdac9f505aeb0065c547704d800826ba338ae659f68607377589d054db0f7)

BibTeX

@misc{dc271bed5656cc,
  title = {Coherent Hierarchical Forecasting for Proportion and Discrete Time Series — arXiv:2609.17918},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.17918},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_271bed5656cc; sha256 112bdac9f505aeb0065c547704d800826ba338ae659f68607377589d054db0f7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.17918. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Hierarchical and grouped time series arise when a multivariate time series is forced to satisfy a set of aggregation constraints, motivating forecast reconciliation methods that ensure coherent forecasts of such hierarchical structures. Many real-world applications involve discrete or bounded supports, introducing additional challenges that are not addressed Gaussian-based reconciliation methods. We develop a post-hoc hierarchical forecasting approach to construct coherent forecast hierarchies for discrete and bounded time series. The method constructs coherent forecasts by convolution and exponential tilting, preserving the distributional properties and the underlying support throughout the hierarchy. We evaluate the proposed approach against state-of-the-art reconciliation methods for both discrete and continuous settings, demonstrating strong performance across a range of experiments. Through simulation studies and empirical applications in epidemiological and demographic data, we show that the method provides reliable and coherent distributional forecasts in challenging scenarios.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-17 in stat.ME).
Categorie arXiv: Methodology (stat.ME) ; Applications (stat.AP).
Pagina del lavoro: https://arxiv.org/abs/2609.17918 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T15:05:19.035Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-09-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).