A Bayesian hierarchical model integrates coherence penalization and level-specific focus into forecasting estimation, yielding improved predictive accuracy on simulated and Australian tourism data.
Probabilistic Forecasting.Annual Review of Statistics and Its Application, 1 (1):125–151, January 2014
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A state-space model with eigenvector-based loadings and AR(1) factors for monthly default counts generates effective copulas and improved annual forecasts via temporal coarse-graining.
A post-processing pipeline applied to ECMWF subseasonal ensembles produces calibrated daily wind power forecasts for France that improve on climatology by 5-15% in CRPS up to 16 days ahead.
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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Temporal Coarse-Graining of Multi-Sector Default Count Data Generates Posterior-Implied Copulas
A state-space model with eigenvector-based loadings and AR(1) factors for monthly default counts generates effective copulas and improved annual forecasts via temporal coarse-graining.
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Achieving Skilled and Reliable Daily Probabilistic Forecasts of Wind Power at Subseasonal-to-Seasonal Timescales over France
A post-processing pipeline applied to ECMWF subseasonal ensembles produces calibrated daily wind power forecasts for France that improve on climatology by 5-15% in CRPS up to 16 days ahead.