A Bayesian hierarchical model integrates coherence penalization and level-specific focus into forecasting estimation, yielding improved predictive accuracy on simulated and Australian tourism data.
Frazier and Christopher Drovandi and Robert Kohn , year=
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MMAF-guided learning trains ensembles of shallow stochastic feed-forward nets under STOU causal/dependence constraints to produce calibrated multi-horizon probabilistic forecasts competitive with ConvLSTM, ConvGRU, and DiffSTG.
Derives closed-form learning rate selector for general posteriors via weighted Fisher divergence to sandwich normal, reducing to Fisher matching rate as special case.
citing papers explorer
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Hierarchical Bayes meets hierarchical forecasting: A flexible framework for level-focused forecasts
A Bayesian hierarchical model integrates coherence penalization and level-specific focus into forecasting estimation, yielding improved predictive accuracy on simulated and Australian tourism data.
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Spatio-temporal probabilistic forecast using MMAF-guided learning
MMAF-guided learning trains ensembles of shallow stochastic feed-forward nets under STOU causal/dependence constraints to produce calibrated multi-horizon probabilistic forecasts competitive with ConvLSTM, ConvGRU, and DiffSTG.
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Learning rate selection via weighted Fisher divergence
Derives closed-form learning rate selector for general posteriors via weighted Fisher divergence to sandwich normal, reducing to Fisher matching rate as special case.