Hierarchical Bayes meets hierarchical forecasting: A flexible framework for level-focused forecasts
Abstract
Decision-making in hierarchical systems requires probabilistic forecasts at all cross-sectional levels. Current hierarchical forecasting methods typically generate independent forecasts at each level and reconcile them post hoc to ensure coherence between upper and lower levels. Such post hoc corrections do not incorporate hierarchical structure or decision goals into the underlying parameter estimation. We propose a fully Bayesian hierarchical forecasting framework that shares information more effectively between and across levels than reconciliation alone. Our approach has the flexibility to softly penalise incoherence, subject to model specification, and to focus the global model and coherence update on hierarchical levels most relevant to decision outcomes. This yields parameter estimates that are focused towards the forecasting goals and capture the requirement for coherency, removing the need to estimate covariance matrices for multi-step forecasting horizons. We demonstrate improvements in predictive accuracy metrics on both simulated data and Australian domestic tourism forecasting.
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BibTeX
@misc{nugteren2026hierarchical,
title = {{Hierarchical Bayes meets hierarchical forecasting: A flexible framework for level-focused forecasts}},
author = {Arwen Nugteren and Mahdi Abolghasemi and Kerrie Mengersen and Christopher Drovandi},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2606.23009},
eprint = {2606.23009},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.23009},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Hierarchical Bayes meets hierarchical forecasting: A flexible framework for level-focused forecasts
AU - Arwen Nugteren
AU - Mahdi Abolghasemi
AU - Kerrie Mengersen
AU - Christopher Drovandi
PY - 2026
DA - 2026/11/11
PB - arXiv
DO - 10.48550/arxiv.2606.23009
UR - https://arxiv.org/abs/2606.23009
AB - Decision-making in hierarchical systems requires probabilistic forecasts at all cross-sectional levels. Current hierarchical forecasting methods typically generate independent forecasts at each level and reconcile them post hoc to ensure coherence between upper and lower levels. Such post hoc corrections do not incorporate hierarchical structure or decision goals into the underlying parameter estimation. We propose a fully Bayesian hierarchical forecasting framework that shares information more effectively between and across levels than reconciliation alone. Our approach has the flexibility to softly penalise incoherence, subject to model specification, and to focus the global model and coherence update on hierarchical levels most relevant to decision outcomes. This yields parameter estimates that are focused towards the forecasting goals and capture the requirement for coherency, removing the need to estimate covariance matrices for multi-step forecasting horizons. We demonstrate improvements in predictive accuracy metrics on both simulated data and Australian domestic tourism forecasting.
ER -