Modeling and Stochastic Learning for Forecasting in High Dimensions / edited by Anestis Antoniadis, Jean-Michel Poggi, Xavier Brossat.

Format:

Book

Contributor:

Antoniadis, Anestis, Editor.

Poggi, Jean-Michel., Editor.

Brossat, Xavier, Editor.

Series:

Lecture Notes in Statistics - Proceedings ; 217

Language:

English

Subjects (All):

Statistics.

Mathematical models.

Computer science--Mathematics.

Computer science.

Mathematical statistics.

Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

Mathematical Modeling and Industrial Mathematics.

Probability and Statistics in Computer Science.

Local Subjects:

Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

Mathematical Modeling and Industrial Mathematics.

Probability and Statistics in Computer Science.

Physical Description:

1 online resource (344 p.)

Edition:

1st ed. 2015.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2015.

Language Note:

English

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.

Contents:

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.

Notes:

Description based upon print version of record.

Includes bibliographical references at the end of each chapters.

ISBN:

3-319-18732-5