Universal Time-Series Forecasting with Mixture Predictors
The author considers the problem of sequential probability forecasting in the most general setting, where the observed data may exhibit an arbitrary form of stochastic dependence. All the results presented are theoretical, but they concern the foundations of some problems in such applied areas as machine learning, information theory and data compression.
| Autor: | Ryabko, Daniil |
|---|---|
| ISBN: | 9783030543037 |
| Sprache: | Englisch |
| Seitenzahl: | 85 |
| Produktart: | Kartoniert / Broschiert |
| Verlag: | Springer International Publishing |
| Veröffentlicht: | 27.09.2020 |
| Schlagworte: | Bayesian Predictors Forecasting Information Theory Machine Learning Theory Nonparametric Statistics Statistics Time Series |
Dr. Daniil Ryabko (HDR) has a full-time position at INRIA, he has recently been on research assignments in Belize and Madagascar.