TY - BOOK ID - 4865199 TI - Modeling and Stochastic Learning for Forecasting in High Dimensions AU - Antoniadis, Anestis. AU - Poggi, Jean-Michel. AU - Brossat, Xavier. PY - 2015 SN - 9783319187327 3319187317 9783319187310 3319187325 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Statistics. KW - Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. KW - Mathematical Modeling and Industrial Mathematics. KW - Probability and Statistics in Computer Science. KW - Computer science. KW - Statistique KW - Informatique KW - Forecasting -- Statistical methods. KW - Stochastic models. KW - Time-series analysis. KW - Mathematics KW - Physical Sciences & Mathematics KW - Mathematical Statistics KW - Forecasting KW - Statistical methods. KW - Models, Stochastic KW - Analysis of time series KW - Mathematical statistics. KW - Mathematical models. KW - Mathematical models KW - Autocorrelation (Statistics) KW - Harmonic analysis KW - Mathematical statistics KW - Probabilities KW - Informatics KW - Science KW - Statistical analysis KW - Statistical data KW - Statistical methods KW - Statistical science KW - Econometrics KW - Statistics . KW - Statistical inference KW - Statistics, Mathematical KW - Statistics KW - Sampling (Statistics) KW - Models, Mathematical KW - Simulation methods UR - https://www.unicat.be/uniCat?func=search&query=sysid:4865199 AB - 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. ER -