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Modeling and Stochastic Learning for Forecasting in High Dimensions

  • Anestis Antoniadis
  • Jean-Michel Poggi
  • Xavier Brossat

Part of the Lecture Notes in Statistics book series (LNS, volume 217)

Also part of the Lecture Notes in Statistics - Proceedings book sub series (LNSP, volume 217)

Table of contents

  1. Front Matter
    Pages i-x
  2. José Blancarte, Mireille Batton-Hubert, Xavier Bay, Marie-Agnès Girard, Anne Grau
    Pages 1-20
  3. Haeran Cho, Yannig Goude, Xavier Brossat, Qiwei Yao
    Pages 35-54
  4. Gerda Claeskens, Eugen Pircalabelu, Lourens Waldorp
    Pages 55-78
  5. Pierre-André Cornillon, Nick Hengartner, Vincent Lefieux, Eric Matzner-Løber
    Pages 79-93
  6. Irène Gijbels, Klaus Herrmann, Dominik Sznajder
    Pages 117-146
  7. Leslie Hatton, Philippe Charpentier, Eric Matzner-Løber
    Pages 147-160
  8. Mathilde Mougeot, Dominique Picard, Vincent Lefieux, Laurence Maillard-Teyssier
    Pages 161-181
  9. Matthew A. Nunes, Marina I. Knight, Guy P. Nason
    Pages 183-192
  10. Pascal Pompey, Alexis Bondu, Yannig Goude, Mathieu Sinn
    Pages 193-212
  11. Till Sabel, Johannes Schmidt-Hieber, Axel Munk
    Pages 213-241

About these proceedings

Introduction

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.

Keywords

Copulas forecasting high dimensional statistics multiscale processes time series

Editors and affiliations

  • Anestis Antoniadis
    • 1
  • Jean-Michel Poggi
    • 2
  • Xavier Brossat
    • 3
  1. 1.Department of StatisticsUniversity Joseph FourierGrenobleFrance
  2. 2.Laboratoire de MathématiquesUniversité Paris-SudOrsay CedexFrance
  3. 3.Electricité de France R & D, OSIRISClamart CedexFrance

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-18732-7
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-18731-0
  • Online ISBN 978-3-319-18732-7
  • Series Print ISSN 0930-0325
  • Series Online ISSN 2197-7186
  • Buy this book on publisher's site