Cargando…
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , |
| 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 |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-18732-7 | ||
| 003 | DE-He213 | ||
| 005 | 20220120173915.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 150604s2015 sz | s |||| 0|eng d | ||
| 020 | |a 9783319187327 |9 978-3-319-18732-7 | ||
| 024 | 7 | |a 10.1007/978-3-319-18732-7 |2 doi | |
| 050 | 4 | |a QA276-280 | |
| 072 | 7 | |a PBT |2 bicssc | |
| 072 | 7 | |a MAT029000 |2 bisacsh | |
| 072 | 7 | |a PBT |2 thema | |
| 082 | 0 | 4 | |a 519 |2 23 |
| 245 | 1 | 0 | |a Modeling and Stochastic Learning for Forecasting in High Dimensions |h [electronic resource] / |c edited by Anestis Antoniadis, Jean-Michel Poggi, Xavier Brossat. |
| 250 | |a 1st ed. 2015. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2015. | |
| 300 | |a X, 339 p. 105 illus., 49 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Lecture Notes in Statistics - Proceedings ; |v 217 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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 advances in observational capabilities and measurement procedures. On June 5-7, 2013, an international Workshop on Industry Practices for FORecasting was held in Paris, France, organized and supported by the OSIRIS Department of Electricité de France Research and Development Division. In keeping with tradition, both theoretical statistical results and practical contributions on this active field of statistical research and on forecasting issues in a rapidly evolving industrial environment are presented. The volume reflects the broad spectrum of the conference, including 16 articles contributed by specialists in various areas. The material compiled is broad in scope and ranges from new findings on forecasting in industry and in time series, on nonparametric and functional methods, and on on-line machine learning for forecasting, to the latest developments in tools for high dimension and complex data analysis. | ||
| 650 | 0 | |a Statistics . | |
| 650 | 0 | |a Mathematical models. | |
| 650 | 0 | |a Computer science-Mathematics. | |
| 650 | 0 | |a Mathematical statistics. | |
| 650 | 1 | 4 | |a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. |
| 650 | 2 | 4 | |a Mathematical Modeling and Industrial Mathematics. |
| 650 | 2 | 4 | |a Probability and Statistics in Computer Science. |
| 700 | 1 | |a Antoniadis, Anestis. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Poggi, Jean-Michel. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Brossat, Xavier. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319187334 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319187310 |
| 830 | 0 | |a Lecture Notes in Statistics - Proceedings ; |v 217 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-18732-7 |z Texto Completo |
| 912 | |a ZDB-2-SMA | ||
| 912 | |a ZDB-2-SXMS | ||
| 950 | |a Mathematics and Statistics (SpringerNature-11649) | ||
| 950 | |a Mathematics and Statistics (R0) (SpringerNature-43713) | ||