Neural network-parameterized regression splines enable joint optimization of forecast quality and stability in distribution-free probabilistic time series models by penalizing dissimilarities from forecast updates.
Bessa, Jethro Browell, and Pierre Pinson
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Contextually-enhanced transformers integrating timetable and occupancy data achieve 26.6% and 56.3% average MAE reductions in railway and building energy forecasting respectively, outperforming prior methods.
Heterogeneous Graph Attention Networks model intra-domain and inter-domain sensor relationships for short-term state forecasting in multi-domain power systems, outperforming baselines by 35.5% NRMSE on a hydroelectric case study.
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Stabilizing distribution-free probabilistic forecasts
Neural network-parameterized regression splines enable joint optimization of forecast quality and stability in distribution-free probabilistic time series models by penalizing dissimilarities from forecast updates.
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Heterogeneous Graph Neural Networks for Short-term State Forecasting in Power Systems across Domains and Time Scales: A Hydroelectric Power Plant Case Study
Heterogeneous Graph Attention Networks model intra-domain and inter-domain sensor relationships for short-term state forecasting in multi-domain power systems, outperforming baselines by 35.5% NRMSE on a hydroelectric case study.