Unsupervised learning on U.S. weather forecast performance
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DOI: 10.1007/s00180-023-01340-w
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- Peter Bauer & Alan Thorpe & Gilbert Brunet, 2015. "The quiet revolution of numerical weather prediction," Nature, Nature, vol. 525(7567), pages 47-55, September.
- Julien Jacques & Cristian Preda, 2014. "Functional data clustering: a survey," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 231-255, September.
- Philippe C. Besse & Herve Cardot & David B. Stephenson, 2000. "Autoregressive Forecasting of Some Functional Climatic Variations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(4), pages 673-687, December.
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Keywords
Weather forecast; Functional principal component analysis; K-means clustering; Discriminative functional mixture model; FunFEM model-based clustering;All these keywords.
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