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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5 Pith papers cite this work, alongside 1,269 external citations. Polarity classification is still indexing.
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GRAFT improves electric load forecasting accuracy by aligning multi-source daily texts with half-hour load series and using cross-attention fusion, outperforming baselines on a new Australian benchmark across hourly to monthly horizons.
KUP-BI distills continuation-style knowledge from a train-only historical library to supply an approximate post-target proxy that is fused into forecasting backbones for improved performance on public datasets.
EnSF with score-based diffusion models improves energy consumption state estimation over open-loop propagation and EnKF under nonlinear observations.
A hybrid deep learning model with physics regularization and SHAP analysis achieves 1.18% MAPE on ERCOT load data and up to 40.5% better performance on extreme events than its individual branches.
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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GRAFT: Grid-Aware Load Forecasting with Multi-Source Textual Alignment and Fusion
GRAFT improves electric load forecasting accuracy by aligning multi-source daily texts with half-hour load series and using cross-attention fusion, outperforming baselines on a new Australian benchmark across hourly to monthly horizons.
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Beyond Extrapolation: Knowledge Utilization Paradigm with Bidirectional Inspiration for Time Series Forecasting
KUP-BI distills continuation-style knowledge from a train-only historical library to supply an approximate post-target proxy that is fused into forecasting backbones for improved performance on public datasets.
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Diffusion Model-Based Data Assimilation for Real-World Energy Consumption Forecasting
EnSF with score-based diffusion models improves energy consumption state estimation over open-loop propagation and EnKF under nonlinear observations.
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Interpretable Physics-Informed Load Forecasting for U.S. Grid Resilience: SHAP-Guided Ensemble Validation in Hybrid Deep Learning Under Extreme Weather
A hybrid deep learning model with physics regularization and SHAP analysis achieves 1.18% MAPE on ERCOT load data and up to 40.5% better performance on extreme events than its individual branches.