Additive stacking for disaggregate electricity demand forecasting

DOI10.1214/20-AOAS1417zbMath1478.62373arXiv2005.10092OpenAlexW3180869686MaRDI QIDQ2245149

Yannig Goude, Christian Capezza, Simon N. Wood, Biagio Palumbo, Matteo Fasiolo

Publication date: 15 November 2021

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2005.10092

zbMATH Keywords

ensemble methodsprobabilistic forecastgeneralised additive modelselectricity demand forecastingregression stacking

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Applications of statistics in engineering and industry; control charts (62P30)

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• R
• GAMLSS
• GitHub
• mgcViz

Cites Work

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• Combining Probability Forecasts
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• Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models
• Prediction, Learning, and Games
• Generalized Additive Models for Location, Scale and Shape