Universal Time-Series Forecasting with Mixture Predictors

Häftad, Engelska, 2020

Del i serien SpringerBriefs in Computer Science

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2020-09-27
Mått:155 x 235 x 6 mm
Vikt:160 g
Format:Häftad
Språk:Engelska
Serie:SpringerBriefs in Computer Science
Antal sidor:85
Förlag:Springer Nature Switzerland AG
ISBN:9783030543037

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT

Mer om författaren

Dr. Daniil Ryabko (HDR) has a full-time position at INRIA, he has recently been on research assignments in Belize and Madagascar.

Recensioner i media

“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)

Innehållsförteckning

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.