東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Modeling and stochastic learning for...

Antoniadis, Anestis.

FindBook

Google Book

Amazon

博客來

Modeling and stochastic learning for forecasting in high dimensions

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Modeling and stochastic learning for forecasting in high dimensions/ edited by Anestis Antoniadis, Jean-Michel Poggi, Xavier Brossat.
其他作者:	Antoniadis, Anestis.
出版者:	Cham :Springer International Publishing : : 2015.,
面頁冊數:	x, 339 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Forecasting - Statistical methods. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-18732-7
ISBN:	9783319187327 (electronic bk.)

Modeling and stochastic learning for forecasting in high dimensions
Modeling and stochastic learning for forecasting in high dimensions[electronic resource] /edited by Anestis Antoniadis, Jean-Michel Poggi, Xavier Brossat. - Cham :Springer International Publishing :2015. - x, 339 p. :ill., digital ;24 cm. - Lecture notes in statistics,2170930-0325 ;. - Lecture notes in statistics ;205..

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.

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 Electricite 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.

ISBN: 9783319187327 (electronic bk.)

Standard No.: 10.1007/978-3-319-18732-7doiSubjects--Topical Terms:

533226
Forecasting
--Statistical methods.

LC Class. No.: QA280

Dewey Class. No.: 519.55

Modeling and stochastic learning for forecasting in high dimensions
LDR:03632nam a2200337 a 4500 001 2007813
003 DE-He213
005 20160122110837.0
006 m d
007 cr nn 008maaau
008 160219s2015 gw s 0 eng d
020 $a 9783319187327 (electronic bk.)
020 $a 9783319187310 (paper)
024 7 $a 10.1007/978-3-319-18732-7 $2 doi
035 $a 978-3-319-18732-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA280
072 7 $a PBT $2 bicssc
072 7 $a PD $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 519.55 $2 23
090 $a QA280 $b .M689 2015
245 0 0 $a Modeling and stochastic learning for forecasting in high dimensions $h [electronic resource] / $c edited by Anestis Antoniadis, Jean-Michel Poggi, Xavier Brossat.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a x, 339 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in statistics, $x 0930-0325 ; $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 Electricite 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 Forecasting $x Statistical methods. $3 533226
650 0 $a Time-series analysis. $3 532530
650 0 $a Stochastic models. $3 764002
650 1 4 $a Statistics. $3 517247
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 1005896
650 2 4 $a Mathematical Modeling and Industrial Mathematics. $3 891089
650 2 4 $a Probability and Statistics in Computer Science. $3 891072
700 1 $a Antoniadis, Anestis. $3 2156836
700 1 $a Poggi, Jean-Michel. $3 2156837
700 1 $a Brossat, Xavier. $3 2156838
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Lecture notes in statistics ; $v 205. $3 1567146
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-18732-7
950 $a Mathematics and Statistics (Springer-11649)