Modeling and stochastic learning for forecasting in high dimensions [electronic resource] / Anestis Antoniadis, Jean-Michel Poggi, Xavier Brossat, editors.
Antoniadis, Anestis, editor.; Poggi, Jean-Michel, editor.; Brossat, Xavier, editor.
2015
QA280
Available Online

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Title Modeling and stochastic learning for forecasting in high dimensions [electronic resource] / Anestis Antoniadis, Jean-Michel Poggi, Xavier Brossat, editors.
ISBN 9783319187327 electronic book
3319187325 electronic book
9783319187310
Published Cham : Springer, [2015]
Language English
Description 1 online resource : illustrations.
Call Number QA280
Dewey Decimal Classification 519.5/5
Summary 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (viewed June 15, 2015).
Added Author Antoniadis, Anestis, editor.
Poggi, Jean-Michel, editor.
Brossat, Xavier, editor.
Series Lecture notes in statistics (Springer-Verlag) ; v. 217.
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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.

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