Universal Time-Series Forecasting with Mixture Predictors (1st ed. 2020) (SpringerBriefs in Computer Science)

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Synopsis
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.
Copyright:
2020

Book Details

Book Quality:
Publisher Quality
ISBN-13:
9783030543044
Related ISBNs:
9783030543037
Publisher:
Springer International Publishing
Date of Addition:
Copyrighted By:
Springer Nature Switzerland AG
Adult content:
No
Language:
English
Has Image Descriptions:
No
Categories:
Nonfiction, Computers and Internet
Submitted By:
Bookshare Staff
Usage Restrictions:
This is a copyrighted book.

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