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Universal Time-Series Forecasting with Mixture Predictors

Universal Time-Series Forecasting with Mixture Predictors

Daniil Ryabko

66,98 €
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Editorial:
Springer Nature B.V.
Año de edición:
2020
Materia
Ciencias de la computación
ISBN:
9783030543037
66,98 €
IVA incluido
Disponible

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Sobre IBD

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.

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    Stationarity is a very general, qualitative assumption, that can be assessed on the basis of application specifics. It is thus  a rather attractive assumption to base statistical analysis on, especially for problems for which less general qualitative assumptions, such as independence or finite memory, clearly fail. However, it has long been considered too general to be able to ...
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