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Modeling and stochastic learning for forecasting in high dimensions [electronic resource]

Modeling and stochastic learning for forecasting in high dimensions [electronic resource]

자료유형
E-Book(소장)
개인저자
Antoniadis, Anestis. Poggi, Jean-Michel. Brossat, Xavier.
서명 / 저자사항
Modeling and stochastic learning for forecasting in high dimensions [electronic resource] / Anestis Antoniadis, Jean-Michel Poggi, Xavier Brossat, editors.
발행사항
Cham :   Springer International Publishing :   Imprint: Springer,   2015.  
형태사항
1 online resource (x, 339 p.) : ill. (some col.).
총서사항
Lecture notes in statistics,0930-0325 ; 217
ISBN
9783319187327
요약
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.
일반주기
Title from e-Book title page.  
내용주기
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.
서지주기
Includes bibliographical references.
이용가능한 다른형태자료
Issued also as a book.  
일반주제명
Statistics. Forecasting --Statistical methods. Time-series analysis. Stochastic models.
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020 ▼a 9783319187327
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084 ▼a 519.55 ▼2 DDCK
090 ▼a 519.55
245 0 0 ▼a Modeling and stochastic learning for forecasting in high dimensions ▼h [electronic resource] / ▼c Anestis Antoniadis, Jean-Michel Poggi, Xavier Brossat, editors.
260 ▼a Cham : ▼b Springer International Publishing : ▼b Imprint: Springer, ▼c 2015.
300 ▼a 1 online resource (x, 339 p.) : ▼b ill. (some col.).
490 1 ▼a Lecture notes in statistics, ▼x 0930-0325 ; ▼v 217
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references.
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.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Statistics.
650 0 ▼a Forecasting ▼x Statistical methods.
650 0 ▼a Time-series analysis.
650 0 ▼a Stochastic models.
700 1 ▼a Antoniadis, Anestis.
700 1 ▼a Poggi, Jean-Michel.
700 1 ▼a Brossat, Xavier.
830 0 ▼a Lecture notes in statistics ; ▼v 217.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-3-319-18732-7
945 ▼a KLPA
991 ▼a E-Book(소장)

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/e-Book 컬렉션/ 청구기호 CR 519.55 등록번호 E14027871 도서상태 대출불가(열람가능) 반납예정일 예약 서비스 M

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