Universal Time-Series Forecasting with Mixture Predictors

By: Daniil Ryabko

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Paperback | 27 September 2020

At a Glance

Paperback
96 Pages

Dimensions(cm)
23.5 x 15.49 x 0.51

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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.

Industry Reviews

"It is a very useful book for graduate students and researchers who are interested in the problem of sequential prediction." (Lei Jin, Mathematical Reviews, November, 2022)
"The author lists some open problems in extending the subject matter discussed in the book. ... The book ... should be of interest for those researchers interested in the study of problems of sequential prediction." (B. L. S. Prakasa Rao, zbMATH 1479.62002, 2022)

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