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Modeling and Stochastic Learning for Forecasting in High Dimensions : Springer Nature Proceedings excluding Computer Science - Anestis Antoniadis

Modeling and Stochastic Learning for Forecasting in High Dimensions

By: Anestis Antoniadis (Editor), Jean-Michel Poggi (Editor), Xavier Brossat (Editor)

eText | 4 June 2015

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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 Electricite 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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Non-FictionMathematicsProbability & StatisticsAlgebraComputing & I.T.Business ApplicationsMathematical & Statistical SoftwareApplied MathematicsMathematical ModellingComputer ScienceMathematical Theory of ComputationMaths for Computer Scientists
Engineering & TechnologyTechnology in GeneralMaths for Engineers

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