Springer Modeling and Stochastic Learning for Forecasting 217

Modeling and Stochastic Learning for Forecasting in High Dimensions (Lecture Notes in Statistics, 217) provides a deep look into the intersection of theoretical understanding and practical application. As forecasting and time series prediction continue to evolve due to better observational capabilities, this volume addresses the growing need for advanced methodologies in high-dimensional settings. This book stems from the international Workshop on Industry Practices for Forecasting held in Paris, France. Organized with support from the OSIRIS Department of Electricite de France Research and Development Division, the content focuses on bridging the gap between academic research and industrial use. It is an essential resource for those studying the mathematical foundations of prediction and the real-world implementation of forecasting models in complex environments.