Modeling and Stochastic Learning for Forecasting in High Dimensions (Paperback, 2015 ed.)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Lecture Notes in Statistics, 217
Release date:	June 2015
Editors:	Anestis Antoniadis • Jean-Michel Poggi • Xavier Brossat
Dimensions:	235 x 155 x 19mm (L x W x T)
Format:	Paperback
Pages:	339
Edition:	2015 ed.
ISBN-13:	978-3-319-18731-0
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > General theory of computing > Mathematical theory of computation Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling
LSN:	3-319-18731-7
Barcode:	9783319187310

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