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Modeling and Stochastic Learning for Forecasting in High Dimensions

The chapters in this volume stress the need for advances in theoretical understanding to go hand-in-hand with the widespread practical application of forecasting in industry. Forecasting and time series prediction have enjoyed considerable attention over the last few decades, fostered by impressive...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Antoniadis, Anestis (Editor ), Poggi, Jean-Michel (Editor ), Brossat, Xavier (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2015.
Edición:1st ed. 2015.
Colección:Lecture Notes in Statistics - Proceedings ; 217
Temas:
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 1 Short Term Load Forecasting in the Industry for Establishing Consumption Baselines: A French Case
  • 2 Confidence intervals and tests for high-dimensional models: a compact review
  • 3 Modelling and forecasting daily electricity load via curve linear regression
  • 4 Constructing Graphical Models via the Focused Information Criterion
  • 5 Nonparametric short term Forecasting electricity consumption with IBR
  • 6 Forecasting the electricity consumption by aggregating experts
  • 7 Flexible and dynamic modeling of dependencies via copulas
  • 8 Operational and online residential baseline estimation
  • 9 Forecasting intra day load curves using sparse functional regression
  • 10 Modelling and Prediction of Time Series Arising on a Graph
  • 11 GAM model based large scale electrical load simulation for smart grids
  • 12 Spot volatility estimation for high-frequency data: adaptive estimation in practice
  • 13 Time series prediction via aggregation: an oracle bound including numerical cost
  • 14 Space-time trajectories of wind power generation: Parametrized precision matrices under a Gaussian copula approach
  • 15 Game-theoretically Optimal Reconciliation of Contemporaneous Hierarchical Time Series Forecasts
  • 16 The BAGIDIS distance: about a fractal topology, with applications to functional classification and prediction.