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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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Online conformal prediction post-processing guarantees calibrated uncertainty coverage for GenCast, NeuralGCM, and AIFS-ENS forecasts of temperature and precipitation including extremes.
Time series foundation models generate counterfactual forecasts showing increased transit ridership and reduced aggregate travel after NYC congestion pricing, with spatial and demographic heterogeneity.
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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Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction
Online conformal prediction post-processing guarantees calibrated uncertainty coverage for GenCast, NeuralGCM, and AIFS-ENS forecasts of temperature and precipitation including extremes.
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Public transit gains and spatially uneven travel demand changes after NYC congestion pricing
Time series foundation models generate counterfactual forecasts showing increased transit ridership and reduced aggregate travel after NYC congestion pricing, with spatial and demographic heterogeneity.