Universal Time-Series Forecasting with Mixture Predictors / SpringerBriefs in Computer Science (PDF)
(Sprache: Englisch)
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...
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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.
Autoren-Porträt von Daniil Ryabko
Dr. Daniil Ryabko (HDR) has a full-time position at INRIA, he has recently been on research assignments in Belize and Madagascar.
Bibliographische Angaben
- Autor: Daniil Ryabko
- 2020, 1st ed. 2020, 85 Seiten, Englisch
- Verlag: Springer International Publishing
- ISBN-10: 3030543048
- ISBN-13: 9783030543044
- Erscheinungsdatum: 26.09.2020
Abhängig von Bildschirmgrösse und eingestellter Schriftgrösse kann die Seitenzahl auf Ihrem Lesegerät variieren.
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- Dateiformat: PDF
- Grösse: 1.37 MB
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Englisch
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