Universal Time-Series Forecasting with Mixture Predictors
Published on 26. September 2020
VIII, 85 pages
978-3-030-54304-4 (ISBN)
Description
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.
Reviews / Votes
"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)
More details
Series
Edition
1st ed. 2020
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
VIII, 85 p. 1 illus.
File size
1,44 MB
ISBN-13
978-3-030-54304-4 (9783030543044)
DOI
10.1007/978-3-030-54304-4
Schweitzer Classification
Other editions
Additional editions
Book
09/2020
Springer
€53.49
Shipment within 7-9 days
Person
Dr. Daniil Ryabko (HDR) has a full-time position at INRIA, he has recently been on research assignments in Belize and Madagascar.
Content
Introduction.- Notation and Definitions.- Prediction in Total Variation: Characterizations.- Prediction in KL-Divergence.- Decision-Theoretic Interpretations.- Middle-Case: Combining Predictors Whose Loss Vanishes.- Conditions Under Which One Measure Is a Predictor for Another.- Conclusion and Outlook.
