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Herausgeber: 
  • Anestis Antoniadis
  • Jean-Michel Poggi
  • Xavier Brossat
  • Modeling and Stochastic Learning for Forecasting in High Dimensions 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Juni 2015  
    Genre:  Schulbücher 
     
    C / Mathematical & statistical software / Mathematical Modeling and Industrial Mathematics / Mathematical modelling / Mathematical models / Mathematical statistics / Mathematics and Statistics / Maths for computer scientists
    ISBN:  9783319187310 
    EAN-Code: 
    9783319187310 
    Verlag:  Springer EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  #217 - Lecture Notes in Statistics
    Lecture Notes in Statistics - Proceedings  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  5329 gr 
    Seiten:  339 
    Illustration:  X, 339 p. 105 illus., 49 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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.

      



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