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Universal time-series forecasting wi...

Ryabko, Daniil.

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Universal time-series forecasting with mixture predictors

Record Type:	Electronic resources : Monograph/item
Title/Author:	Universal time-series forecasting with mixture predictors/ by Daniil Ryabko.
Author:	Ryabko, Daniil.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	viii, 85 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Time-series analysis - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-030-54304-4
ISBN:	9783030543044

Universal time-series forecasting with mixture predictors
Ryabko, Daniil.

Universal time-series forecasting with mixture predictors[electronic resource] /by Daniil Ryabko. - Cham :Springer International Publishing :2020. - viii, 85 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

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.

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.

ISBN: 9783030543044

Standard No.: 10.1007/978-3-030-54304-4doiSubjects--Topical Terms:

700459
Time-series analysis
--Data processing.

LC Class. No.: QA280 / .R93 2020

Dewey Class. No.: 519.55

Universal time-series forecasting with mixture predictors
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505 0 $a 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.
520 $a 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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