Universal Time-Series Forecasting with Mixture Predictors by Daniil Ryabko
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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“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)
“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)
Dr. Daniil Ryabko (HDR) has a full-time position at INRIA, he has recently been on research assignments in Belize and Madagascar.
| SKU | Unavailable |
| ISBN 13 | 9783030543037 |
| ISBN 10 | 303054303X |
| Title | Universal Time-Series Forecasting with Mixture Predictors |
| Author | Daniil Ryabko |
| Series | Springerbriefs In Computer Science |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer Nature Switzerland AG |
| Year published | 2020-09-27 |
| Number of pages | 85 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |