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
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
verdicts
UNVERDICTED 5representative citing papers
A low-rank dynamic factor model with AR(1) latent states and binomial observations, when aggregated over time, generates horizon-dependent posterior-implied copulas that reproduce annual eigenvalue amplification on S&P sector default data and improve some forecast scores.
Diffusion model improves GFS/GEFS ensemble CAPE forecasts and incorporates aerosol optical depths for additional gains.
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.
Current proper scoring rules in probabilistic electricity price forecasting prioritize sharpness at the expense of calibration, leading to overconfident and unreliable uncertainty estimates.
citing papers explorer
-
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.
-
Investigating Calibration Challenges in Probabilistic Electricity Price Forecasting
Current proper scoring rules in probabilistic electricity price forecasting prioritize sharpness at the expense of calibration, leading to overconfident and unreliable uncertainty estimates.